Making Work Pay in Nicaragua: Employment, Growth, and Poverty Reduction (Directions in Development)

D I R E C T I O N S I N D E V E L O P M E N T

Poverty

Making Work Pay in NicaraguaEmployment, Growth, and Poverty Reduction

Catalina Gutierrez, Pierella Paci, and Marco Ranzani

Poor people derive most of their income from work. However, there is insufficient under-standing of the role of employment and earnings as a link between growth and povertyreduction, especially in low-income countries. The Making Work Pay series analyzes theimportant roles of labor markets, employment, productivity, and labor income in facilitatingshared growth and promoting poverty reduction.

Making Work Pay in Nicaragua provides a description of the trends in growth, poverty andlabor market outcomes in Nicaragua. It assesses the linkages among changes in output,employment, and labor productivity and links changes in the quality and quantity ofemployment to poverty reduction. The book also addresses other key issues such as ruralversus urban conditions, women and children in the labor market, self-employment andhousehold enterprises, and it identifies priorities for further analysis and policy intervention.

Making Work Pay in Nicaragua will be of interest to development practitioners in interna-tional organizations, governments, research institutions, and universities with an interestin inclusive growth and the creation of productive employment.

ISBN 978-0-8213-7534-1

SKU 17534

Making Work Pay in Nicaragua

Making Work Pay in Nicaragua Employment, Growth, and Poverty ReductionCatalina GutierrezPierella PaciMarco Ranzani

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ISBN-13: 978-0-8213-7534-2eISBN: 978-0-8213-7535-8DOI: 10.1596/978-0-8213-7534-1

Library of Congress Cataloging-in-Publication Data

Making work pay in Nicaragua: employment, growth, and poverty reduction /edited by Catalina Gutiérrez, Pierella Paci, Marco Ranzani.

p. cm.Includes bibliographical references and index.ISBN 978-0-8213-7534-1 -- ISBN 978-0-8213-7535-8 (electronic)

1. Labor market--Nicaragua. 2. Wages--Nicaragua. 3. Poverty--Nicaragua. 4. Laborproductivity--Nicaragua. I. Gutiérrez, Catalina. II. Paci, Pierella, 1957- III. Ranzani,Marco, 1979- HD5737.A6M34 2008331.1097285--dc22

2008017702

Cover design: Candace Roberts, Quantum Think, Philadelphia, PA, United States

Acronyms and Abbreviations xiAcknowledgments xiii

Chapter 1 Introduction and Overview 1Objectives and Scope of this Task 2Structure of the Report 3

Chapter 2 Country Context 7Macroeconomic Context 7Labor Market Context 10Labor Regulation in Nicaragua 20

Chapter 3 Output, Population, Employment, and Poverty 27Main Trends in Output 27Main Trends in Population 28Main Trends in Employment 30Main Trends in Poverty 31Decomposition of Per Capita Income Growth 32

Contents

v

A Closer Look at the Manufacturing Sector 42Annex 3A. Decomposition of Per Capita 48

Value Added Growth

Chapter 4 Employment and Labor Income Profile 59of the PopulationIncome and Employment Profile 60Decomposition of Changes in Labor Income 63A Closer Look at Agriculture 69Annex 4A. Decomposition of Labor Income 78

GrowthAnnex 4B. Estimation Results 81

Chapter 5 Segmentation and Skill Mismatch 87Labor Market Segmentation: Basic Assumptions

and Literature Review 87Evidence of Segmentation across Different

Dimensions 90Segmentation and Barriers to Mobility:

A Qualitative Approach 103Skill Mismatch 107

Chapter 6 Policy Implications and Further Research 115

References 119Index 125

Figures2.1 Investment, Exports, and Growth, 1995–2005 92.2 Distribution of Wages by Sector and Formality, 2001 243.1 Change in Population Structure, 2001–05 293.2 Share of Employment by Sectors, 2001 and 2005 313.3 Aggregate Employment and Productivity Profile of 35

Growth, 2001–053.4 Decomposition of Changes in Output per Worker, 37

2001–054.1 Growth in Average Per Capita Income, by Quintile, 2001 684.2 Productivity of Sensitive Products by Yield per Hectare, 71

1990–2005

vi Contents

4.3 Relative Productivity by Product, 1990–2005 724.4 Area Harvested for Sensitive Products, 1990–2005 734.5 Production Volume for Sensitive Products, 1990–2005 744.6 Producer Prices Relative to Consumer Prices for 75

Export Goods, 1999–20064.7 Relative Prices of Trade for Meat, 1999–2006 754.8 Relative Prices for Cereals, 1999–2006 764.9 Relative Prices for Sensitive Products, 2001–06 765.1 Hourly Earnings by Employment Category, 2001 935.2 Hourly Earnings by Broad Sector and Informality, 2001 945.3 Changes in the Skill Premium and the Relative Supply 109

of Skills, of Total Wage Workers, 2001–055.4 Changes in the Skill Premium and the Relative Supply 110

of Skills, of Urban Wage Workers, 2001–05

Tables2.1 Main Macroeconomic Indicators, 1998–2005 112.2 Main Indicators of the Labor Market, 2001 and 2005 132.3 Earnings and Income by Employment Category, 14

2001 and 20052.4 Hierarchical Description of the Population Six Years 16

of Age and Above, 2001 and 20052.5 Other Characteristics of the Employed, 2001 and 2005 182.6 Labor Market Flexibility, Comparative Performance 212.7 Issues Affecting the Investment Climate 222.8 Minimum Wage and Lowest Wage Paid as a Proportion 23

of Minimum Wage, 2001 and 20053.1 Sectoral Growth, 1998–2005 283.2 Average Level of Education of Population Ages 25 to 64 303.3 Evolution of Employment by Sectors, 2001 and 2005 323.4 Headcount Poverty Rates of the Working-Age Population 33

by Employment Status, 2001–053.5 Employment by Sector and Poverty Level, Shares of 34

Total Employment, 2001 and 20053.6 Percentage Change in Selected Variables, 2001–05 353.7 Decomposition of Intersectoral Shifts 393.8 Sectoral Growth, 2001–05 39

Contents vii

3.9 Employment Shares and Productivity, by Sectors of 40Economic Activity, 2001–05

3.10 Total Sectoral Contribution to Growth, 2001–05 413.11 Wages by Sector of Economic Activity, 2001 and 2005 423.12 Employment Generation by Subsector, 2001 and 2005 433.13 Wages in the Manufacturing Sector, 2001 and 2005 443.14 Employment Generation in Manufacturing by Type 44

of Employment, 2001 and 20054.1 Employment Status of the Working-Age Population 61

by Quintile, 2001 and 20054.2 Employment Status of the Working-Age Population by 61

Poverty Level, 2001 and 20054.3 Employment Categories by Quintile, 2001 and 2005 624.4 Employment Categories by Poverty Level, 2001 and 2005 624.5 Structure of Income by Quintile, 2001 and 2005 644.6 Structure of Income by Poverty Level, 2001 and 2005 644.7 Labor Profile of the Population by Poverty Level,

2001 and 2005 654.8 Labor Profile of the Population by Quintile, 67

2001 and 20054.9 Per Capita Household Income Changes, by Quintile, 68

2001–054.10 Shapley Decomposition of Per Capita Labor Income, 70

by Quintile4.11 Number of Farms, by Sensitive Product, according to 71

Farm Size, 20014B.1 Mean and Standard Deviation, by Employment 81

Category4B.2 Mean and Standard Deviation, by Sector of Economic 82

Activity and Formality Level4B.3 Earnings Equations by Employment Category, 2001 834B.4 Earnings Equations by Sector of Employment, 2001 844B.5 Oaxaca-Blinder Decomposition: Detailed Outcomes 85

for Sector and Informality4B.6 Oaxaca-Blinder Decomposition: Detailed Outcomes 86

for Employment Categories

viii Contents

5.1 Selection among Employment Categories, 2001 975.2 Selection among Sectors, 2001 985.3 Oaxaca-Blinder Decomposition by Employment 100

Category5.4 Oaxaca-Blinder Decomposition by Employment Sector 1015.5 Reason for Starting a Business, by Level of Education, 104

20015.6 Reason for Starting a Business, by Poverty Level, 2001 1055.7 Skills and Education of Available Workers as an 112

Obstacle to Firms’ Operation and Growth, 2003

Boxes1.1 Definitions 42.1 Urban versus Rural Population: Possible Data Problems 193.1 Evolution of the Maquila Sector and Its Importance in 45

the Employment Growth in Manufacturing

Contents ix

BCN Central Bank of NicaraguaCAFTA Central American Free Trade Agreement CEPAL Economic Commission for Latin America and

the CaribbeanCPI consumer price indexEMNV National Household Living Standards Survey

(Encuesta de Medición del Nivel de Vida) EPZ export processing zoneFAO Food and Agriculture Organization of the United NationsGDP gross domestic productHIPC heavily indebted poor countriesIFC International Finance CorporationILO International Labour OrganizationIMF International Monetary FundINATEC National Technology Institute

(Instituto Nacional Tecnológico)INEC National Institute of Statistics and Census

(Instituto National de Estadísticas y Censos)

Acronyms and Abbreviations

xi

xii Acronyms and Abbreviations

INIDE National Institute for Development InformationMITRAB Ministry of Labor PRGF Poverty Reduction and Growth FacilityRAAS Autonomous Region of the Atlantic South

(Región Autónoma del Atlántico Sur)RRR relative risk ratio

This report was prepared by Catalina Gutierrez, Pierella Paci and MarcoRanzani in the Jobs and Migration cluster in the Poverty Reduction andDevelopment Effectiveness Group at the World Bank as part of a multi-year program on the role of employment for inclusive growth. It also pro-vided background information for the Poverty Assessment of Nicaraguaproduced by the World Bank.

The team would like to thank very much a number of people withoutwhom this report would not have been possible. First, the Minister ofLabor of Nicaragua, Janeth Chavéz Gómez, for taking time to provide uswith detailed explanations of the characteristics and peculiarities of theNicaraguan labor market and for sharing with us her view on priorityissues. Second, the staff of the Central Bank of Nicaragua, who havealways responded positively to our many data and information requests.The team is particularly indebted to the Director of the ResearchDepartment, Mario Alemán, and, among his staff, Hiparco Loaisiga, JesusRojas, Ligia Miranda, Miguel Aguilar, and Lisbeth Laguna, who sharedtheir knowledge and views with us in addition to providing us withinvaluable data and information. The team is also grateful to Juan Rocha

Acknowledgments

xiii

xiv Acknowledgments

of the Statistical and Census Office in Nicaragua for his help with house-hold surveys and to Alejandro Martinéz Cuenca in FundaciónInternacional para el Desafío Económico Global (FIDEG), who was kindenough to share his views on the challenges faced by the Nicaraguaneconomy. Finally, special thanks go to Nydia Betanco in the NicaraguaWorld Bank Country Office for all her help and support while on missionin Nicaragua.

The report benefited greatly from the comments received from themembers of the Nicaragua Poverty Assessment Team, led by FlorenciaCastro-Leal, and from the participants to the seminar held in Managua,March 16–17, 2007. We are particularly grateful to Florencia, NormanHicks, Gabriel Demombynes, Diego Angel-Urdinola, Ximena Del Carpio,and José Ramón Laguna for helpful comments and advice. The manyinputs of the members of the Employment and Migration team in thePoverty Reduction and Debt Effectiveness Unit at the World Bank werealso invaluable. The team is extremely grateful for these inputs.

The degree to which growth is able to translate into poverty reductiondepends on how its benefits are distributed among different segments ofsociety. There is little doubt that growth—measured by changes in aver-age income—contributes significantly to poverty reduction.1 However, itis also clear that countries differ in the degree to which income growthspells have translated into poverty reduction. Although differences in theresponsiveness of poverty to income growth account for a small fractionof the overall differences in poverty changes across countries, from thepoint of view of an individual country, these differences may have signif-icant implications for poverty reduction, especially in the short term.2

There is a general consensus that the availability of employmentopportunities and their characteristics constitute an essential transmis-sion channel from growth to poverty reduction and, in this way, play akey role in poverty’s response to growth. For one thing, the poor derivemost of their income from work, either as self-employed or as employees,so what happens to their income and employment status seems tautolog-ically relevant. In addition, the ease with which the poor may take up theopportunities afforded by growth may depend crucially on (i) the struc-ture of employment, (ii) the returns to labor and their distribution, and

C H A P T E R 1

Introduction and Overview

1

(iii) the existence of imperfections and frictions in the labor markets. Forexample, one may be inclined to believe that when the poor face flexiblelabor markets and low barriers to mobility across labor market segments,geographic regions, or sectors of production, they are in a better positionto take the opportunities generated by growth, by moving more easily tothe growing sectors. Similarly, the effectiveness of growth in reducingpoverty may also depend on whether growth is unskilled labor–intensiveand whether the poor have or can easily acquire the skills required by thegrowing sectors. Moreover, there is some evidence of strong links betweenlabor market regulations, such as minimum wages, and the incidence ofpoverty in developing countries.

The concern that employment, returns to labor, and imperfections orrigidities in the labor markets play a crucial role in the poverty impact ofgrowth has been reflected in the emphasis in the policy debate on theidea that jobless growth has been responsible for the disappointing resultsseen by some countries in the effectiveness of growth in reducing pover-ty. As a result, debates addressing how to foster employment-intensivegrowth have followed.3 However, it is also often recognized that povertyis less an outcome of open unemployment than of adequate levels ofincome, and as such, emphasis should be placed not on increasingemployment levels but on increasing the productivity of the workingpoor (ILO 2003). The debate has also been concerned with whether pol-icy interventions should concentrate on increasing earnings in the sectorswhere the poor are found (such as agriculture), or whether they shouldbe targeted to sectors where the poor are not found, so that more of thepoor can be drawn into the higher-earning sectors (Fields 2006). To date,there is very little evidence to illuminate the debate. Moreover, the ques-tions are hard to address, because there is lack of clarity on how to achievethe alternative objectives and because it is inherently difficult to identifythe costs and benefits of the possible policy alternatives.

Objectives and Scope of this Task

The objective of this report is to shed light on some of the issues dis-cussed above in the case of Nicaragua, and to provide some policy guide-lines for the fight against poverty. In particular, it hopes to be able to iden-tify the growing sectors, as well as the constraints faced by the poor inbenefiting from this growth.

2 Making Work Pay in Nicaragua: Employment, Growth, and Poverty Reduction

Introduction and Overview 3

The report is part of a series of studies conducted within the PRMPRto foster understanding of the role of employment earnings and labormarkets in shared growth. In addition, it is intended to function as a back-ground document for the World Bank’s Nicaragua Poverty Assessment2007.

Structure of the Report

The report is structured in six chapters. Chapter 2 briefly describes theevolution of the Nicaraguan economy, in terms of its macroeconomicindicators, employment, and poverty. The third chapter analyzes the pro-file of growth and the way in which it helps explain the observed behav-ior of poverty, using data from national accounts and employment datafrom household surveys. It describes growth and employment by the sec-tor of economic activity and its employment productivity profile. Thechapter goes more deeply into the evolution of the manufacturing sectorand the maquila production. Chapter 4 looks at the income profile of thepopulation, using household surveys. Segmentation and skill mismatchare explored in chapter 5, and chapter 6 provides a brief statement onpolicy implications and further research.

Definitions of terms used throughout the report are presented below.Workers have been classified into four occupational categories: waged andsalaried workers, individual self-employed workers, family enterpriseworkers, and employers. These are considered qualitatively distinct typesof labor. Each might constitute a segment within the labor market, withdifferent rules for earnings determination and different employment poli-cies for individuals of identical productivity, and workers’ mobilitybetween these employment categories might be limited. The nonwageworkers are divided into the above-mentioned categories for several rea-sons. First, employers (those who employ paid labor) receive substantial-ly higher income than other nonwage workers and are better educated.They often have assets that other nonwage workers do not. Second,returns to labor for family enterprise workers and the self-employed whoare not working with other members of the family need differentmethodologies of calculation. While the income reported by the self-employed working alone is the return for labor for his or her individualwork, reported income for self-employed workers working with other


Box 1.1

Definitions

Employment

Child labor A child between 6 and 14 years of age who performed market activities for at least one hour in the week prior to the survey, or who has a permanent job.

Employed An individual who performed market activities for at least one hour in the week prior to the survey, or who has a permanent job.

Formal Employment for which social security contributions are paid employment by workers and firms.

Household A self-declared self-employed person living in a household enterprise worker, with other self-employed or unpaid family workers.family enterprise worker

Inactive A person who is neither employed nor actively looking for work.

Labor force The sum of the working-age employed and unemployed.

Labor market The place where labor services are bought, sold, and exchanged. The labor market comprises wage and salaried workers and their employers, but also nonwage family enterprise workers and the self-employed, who make up the largest share of workers in Nicaragua.

Maquila sector The maquila sector comprises all production units located in the special export processing zones (EPZs), which are clearly defined zones, often within a wired complex. Produc-tion is undertaken with mostly imported materials using local labor, and all output is destined for export markets. Employment in the maquila sector is referred to as maquila employment.

Self-employed A self-declared self-employed person, living in a household in which there are no other self-employed or unpaid family workers.

Unemployed A working-age individual who is not employed but is actively looking for work.

(continued)

Introduction and Overview 5

Box 1.1

(continued)Wage worker A worker who has declared being salaried for his or her work.

It includes those self-reported as jornaleros and peones, who work for a daily or per job rate in manual agricultural labor, often only during the harvest season.

Working-age The population between 15 and 64 years of age.population

Earnings

Earnings, labor All cash payments, payments in kind, and benefits received income in exchange for labor services in wage and salaried employ-

ment, self-employment, and other forms of labor exchange. Earnings and labor income are used interchangeably, although the latter is more often used when referring to the labor income of a household rather than of an individual. Depending on the context, earnings include only primary job earnings (for example, when comparing earnings in the different sectors) or the sum of earnings in all reported jobs.

Earnings of the For nonagricultural work, it is calculated as declared in the self-employed survey. For agricultural work, it is calculated as net profits and employers using the survey’s agricultural enterprise module.

Household For nonagricultural work, earnings for each individual are enterprise calculated as a proportion of the sum of earnings declared earnings in the survey of all the workers employed in the household

enterprise. For 2001, each worker is assigned a portion of earnings proportional to reported hours of work. For 2005, total enterprise income is divided equally among total number of adult workers. For agricultural work, earnings are derived from the survey’s agricultural enterprise module and divided by the number of adult household members reported as working in the enterprise.

Low earner An employed individual whose earnings are below the national poverty line.

Wage earnings Total cash and in-kind earnings as declared in the survey.


unpaid family members is the income earned by all the family members,and a methodology has to be devised to assign a proportion of householdincome to each member of the family. Finally, individual self-employedare more prevalent in urban areas, whereas family household enterpriseworkers are more prevalent in rural agricultural work.

Notes

1. Kraay (2006) finds that in the short and medium terms income growthaccounts for 70 percent of the variation in headcount poverty, and in the longrun, it accounts for as much as 97 percent.

2. For evidence on heterogeneity in the poverty impact of growth, see for exam-ple Bourguignon (2002); Kakwani, Khandker, and Son (2006); Lucas andTimmer (2005); and Ravallion (2004). See Ravallion (2004) for a discussion ofthe relevance of this heterogeneity from the perspective of a country. A 1 per-cent increase in income levels could result in a poverty reduction of as muchas 4.3 percent or as little as 0.6 percent.

3. One of the core elements of the global employment agenda, MacroeconomicPolicies for Growth and Employment, calls for addressing four key questions,one of which is: How can the employment intensity of growth be increased?(ILO 2003).

This chapter briefly describes the main features of the Nicaraguan econ-omy and its labor market. It summarizes the recent evolution of the mainmacroeconomic indicators, presents a broad picture of the labor marketand how its structure compares with that of other countries, and discuss-es in some detail labor market regulation and its effects on employmentgeneration and investment.

Macroeconomic Context

Over the past 12 years, Nicaragua has witnessed a very significant trans-formation: from a nation torn by war, political instability, and natural dis-asters with its economy plunged into chaos, it has reemerged as an inclu-sive democracy where the foundations for economic growth andsustainable development are being laid. Notwithstanding this progress,Nicaragua still remains among the poorest countries in the western hemisphere. It is classified as a lower-middle-income economy with a percapita gross national income of US$1,000 in 2005, which is a third of theaverage value for Latin America and the Caribbean region and half theaverage of all lower-middle-income countries. It has a population of 5.1million, with a life expectancy at birth of 70 years.

C H A P T E R 2

Country Context

7


During the past 10 years Nicaragua has experienced modest growthrates, averaging 3.8 percent between 1998 and 2005. The country hasconsolidated its structural adjustment programs and completed therequirements for benefiting from the Heavily Indebted Poor Countries(HIPC) Initiative, thereby freeing the country from a debt service thatamounted to 9.5 percent of GDP in 2001.

Between 1998 and 2001, GDP per capita grew at an average rate of3.8 percent and then decelerated, averaging a per capita growth rate of1.7 percent between 2001 and 2005. This growth has been closely tied toinvestment and exports (see figure 2.1). Investment has been fueled byforeign assistance. In 1998, after Hurricane Mitch struck the country,massive reconstruction efforts were undertaken. The country receivedUS$250 million in emergency assistance, and a further US$1.4 billion waspledged by the international community. Until 2001, recovering from theaftermath of the hurricane was a prime policy objective, which, togetherwith important flows of foreign assistance, led to an increase in publicinvestment of 27 percent in 1999.

The past 10 years have also seen a consolidation in the InternationalMonetary Fund (IMF)-led stabilization policies adopted in the early1990s, which were concentrated in controlling hyperinflation, reducingthe fiscal deficit, and privatizing public utility companies. A second waveof reforms was initiated in 2002 with the signature of the PovertyReduction and Growth Facility (PRGF) with the IMF. Its aim was toachieve fiscal sustainability through the broadening of the tax base, theelimination of tax exemptions, improved revenue collection, more effec-tive budgeting, and the improvement of the financial position of theCentral Bank. The government also sought access to a HIPC Initiative togain foreign debt relief. In 2004, Nicaragua reached the completion pointunder HIPC, and bilateral and multilateral debt relief was granted fordebt incurred prior to 2005. On the international front Nicaragua hassigned several trade and integration agreements with its CentralAmerican partners, and trade with El Salvador, Guatemala, andHonduras is gaining in importance, although the United States remainsthe main trading partner.

There have been no major changes in the sectoral structure of produc-tion and in the urban versus rural composition of the population.However, Nicaragua has experienced an important demographic transi-tion: the share of working-age population (15–64 years) has increasedfaster than other age ranges, reducing the dependency ratio.1 In addition,

Country Context 9

the maquila sector (enterprises working in the export processing zones)and financial intermediation have experienced important developments.

Population growth has slowed down, and Nicaragua has started to seea change in its demographic structure. Between 1995 and 2005, the pop-ulation grew at a rate of 1.6 percent annually, a number well below theprojected growth rate of 2 percent. The ratio of working-age population

public investment GDP growth

investment exports GDP growth

8

7

6

5

4

3

2

1

0

8

7

6

5

4

3

2

1

0

2,500

2,000

1,500

1,000

500

0

10,0009,0008,0007,0006,0005,0004,0003,0002,0001,000

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

(a) investment and exports

(b) investment

GD

P g

row

thG

DP

gro

wth

19

94

C$

mill

ion

s1

99

4 C

$ m

illio

ns

Figure 2.1 Investment, Exports, and Growth, 1995–2005percent

Source: Authors’calculations with data from the Central Bank of Nicaragua (BCN).


(15–64 years) to total population increased from 53 percent in 1998 to55 percent in 2001 and 58 percent in 2005, significantly reducing thedependency ratio. Despite this overall demographic change, there was lit-tle gain in the share of urban population, which increased its share in totalpopulation by 1 percentage point in the past 10 years (table 2.1).

The sectoral structure of GDP remained relatively constant duringthese 10 years, with the secondary sector gaining only a 1-percentage-point share during the whole period. Although there were no majorchanges in the structure of production, within the secondary and tertiarysectors there were some important developments, namely the growth ofthe maquila sector and an important surge in financial intermediation.

Financial intermediation has grown at an average annual rate of 9 per-cent. This increase in intermediation is an important development, asNicaragua has the smallest banking system in Central America and as it isthe main source of credit for the private sector. Still, financial intermedi-ation is weak and accounts for only 3.6 percent of GDP.

Growth in the maquila sector has had important implications in termsof the availability of foreign reserves and employment. The maquila sec-tor, which started in the early 1990s with the development of the firstpublic free trade zone, has experienced an amazing dynamism. Between2001 and 2005, the share of maquila exports in total exports jumpedfrom 32 percent to 50 percent, and the sector generated just over 53,000new jobs during these four years. The value of transformation services inthe maquila, which measure, the value of domestic inputs used in theprocess, reached 5.5 percent of total value added in 2005.2

Labor Market Context

The labor market profile of Nicaragua is very similar to that of low-incomeand low-middle-income countries and is characterized by low unemploy-ment rates,3 low formality and wage employment rates, high shares of pop-ulation working in agriculture, and relatively high child labor.

This structure of employment is mainly a reflection of the stage ofindustrialization of these countries. Low- and middle-income countriesstill have a large agricultural sector in which productivity is generally lowand workers are mostly self-employed. Most of the population has verylow incomes, so they cannot afford to be unemployed. Instead, an impor-tant fraction of the working-age population is self-employed in informalactivities, many in agriculture. As industrialization progresses, the share of

Table 2.1 Main Macroeconomic Indicators, 1998–2005

1998 1999 2000 2001 2002 2003 2004 2005

GDP real growth (%) 3.7 7.0 4.1 3.0 0.8 2.5 5.1 4.0Real GDP per capita growth (%) 2.0 5.3 2.4 1.3 –0.9 0.8 3.4 2.2Share of value added in 21.3 20.8 22.3 22.1 21.8 21.6 21.3 21.2

primary sector (%) Share of value added in 26.7 27.5 27.1 27.5 27.1 26.8 27.6 27.8

secondary sector (%)Private consumption per 3.0 4.1 3.5 3.1 2.7 0.1 1.9 1.8

capita real growth (%)Gross fixed investment 4.3 27.1 –16.8 –8.4 –7.1 –1.0 4.2 10.1

real growth (%)Consumer price inflation 13.04 11.22 11.55 7.36 3.99 5.15 8.44 9.42

(year-to-year % change)Real effective exchange rate 98.9 96.9 100.0 100.9 96.9 91.2 89.0 88.7

(year 2000 = 100)Urban population as a share 54.9 55.0 55.2 55.3 55.5 55.6 55.8 55.9

of total populationTotal population (thousands) 4,579 4,655 4,733 4,812 4,892 4,974 5,057 5,142

Sources: National Statistical Institute (INEC), BCN, and World Bank.

Country Context 11

employment in the modern sectors, mainly manufacturing and services,rises. Industrialization spreads predominantly in urban areas, which leadsto a process of urbanization, as rural workers leave low-productivity jobsin agriculture in search of higher paying jobs outside of agriculture.

As urbanization progresses, urban self-employment in low-productivi-ty jobs (in many cases informal) increases.The reason behind this increaseis still a matter of debate and may depend on the particulars of the labormarket structure and regulation. In many cases workers who are search-ing or queuing for good jobs are still too poor to afford to be unemployedand must engage in self-employment “survival activities” while they seeka job. In other cases, monopsonistic behavior by firms leads to very lowwages for the unskilled, so that many low-skilled migrant workers findself-employment as attractive as wage employment. With urbanization,unemployment begins to be noticeable, as higher incomes resulting fromhigher productivity permit the luxury of shopping for good jobs.

The development of a modern sector also comes with a rise in formal-ization and in the share of wage and salary employment, as modern firmsgrow and demand labor. The growth of the modern sector is usually alsoaccompanied by a rise in agricultural productivity, although the links and


causalities for this are less clear. In many cases purposeful investment inagriculture frees rural labor, as more productive techniques mean thatfewer workers are needed to exploit the available land. This surplus labormigrates to urban markets, providing new labor that feeds the process ofurbanization and industrialization. In other cases, migration to nonagri-cultural jobs with higher productivity and higher pay allows householdsto generate savings that can translate into new investments that raise agri-cultural productivity.

As industrialization progresses child labor may decrease, as higherincomes mean lower opportunity costs of sending children to school.Additionally, as the demand for skill increases, so do its returns, and thebenefits of acquiring an education become more evident. Thus, it is cost-lier not to send children to school.

In Nicaragua, unemployment as defined by the International LabourOrganization (ILO) is low, slightly less than 4 percent (see table 2.2).Most of the employed work in the informal sector (82 percent), wageemployment accounts for half of the employed, agriculture absorbs a highshare of employment (37 percent), and child labor is relatively high (9percent). Agriculture is still a sector with low returns, and productivityhas been declining, suggesting that employment in this sector still acts asa “last resort” option for the working population. In the discussion thatfollows, the labor market structure is described in more detail.

Table 2.2 presents the main indicators of the labor market. It showsthat unemployment rates according to the ILO definition are very lowand that they remained almost constant during the period under analysis.The broad unemployment rate, which also includes discouraged workers,is slightly higher but is still low compared with other countries (less than10 percent). The working-age population, defined as those ages 15–64, asa proportion of the total population increased 5 percent (or 3 percentagepoints). The number of employed as a fraction of the total working-agepopulation also rose slightly. Child labor saw a small increase, from 8.7percent to 9.2 percent. The poverty rate among unemployed workersstands out, at half the overall poverty rate, which suggests that unemploy-ment is not strongly correlated with poverty.

The table also shows the number of workers affiliated with social secu-rity, which is often a measure for formalization. In Nicaragua only some 19percent of the labor force has social security, and this ratio decreasedslightly in 2005. Finally, the table shows the number of workers holdingmore than one job concurrently. It has been pointed out that in many cases

workers cannot generate enough income from their main job and mustfind additional work to complement their income, so that the share ofworkers holding two or more jobs concurrently is often used as a measureof the (poor) quality of the jobs.The share of workers holding two or morejobs is less than 10 percent, a figure below that of low-income countries.

Agricultural jobs offer the lowest returns. Outside of agriculture, theself-employed do not earn less per hour worked than the wage employed,but they appear to earn less annually owing to shorter spells of work dur-ing the year.

Table 2.3 shows the median annual labor income and median earningrates for the different employment categories. Earnings are lower for all

Country Context 13

Table 2.2 Main Indicators of the Labor Market, 2001 and 2005

Level in 2001 Level in 2005 % change

Unemployment ratea 3.47 3.39 –2.44Broad unemployment rateb 8.07 6.87 –14.84Ratio of employment to working-age 62.15 62.78 1.01

populationWorking-age population as a fraction of 55.30 58.22 5.27

total populationChild labor rate 8.87 9.28 4.69Share of long-term unemployedc 0.04 1.50 3,650Poverty rate among unemployed workers 22.14 28.52 28.80

(national poverty line, poor)Poverty rate among unemployed workers 6.96 6.69 –3.83

(national poverty line, extremely poor)Poverty rate among unemployed workers 24.76 28.52 15.19

(international poverty line, 1$/day)Share of workers holding two or more 9.03 8.41 –6.92

jobs concurrentlyd

Share of workers affiliated with 19.70 18.05 –8.40social securitye

Source: Authors’calculations using data from the National Household Living Standards Survey (EMNV 2001, 2005).

a. ILO definition of unemployment is those who are not employed but who actively searched for a job in the pastweek.

b. Broad unemployment rate also includes discouraged workers.

c. Ratio of long-term unemployed over total active labor force = (employed + unemployed), the questions are notstrictly comparable: in 2001, How long have you been unemployed? In 2005, How long have you been searchingfor a job?

d. Defined as holding two jobs in the past week.

e. Affiliated with social security in main occupation.

14 Making Work Pay in Nicaragua: Employment, Growth, and Poverty ReductionTa

ble

2.3

Earn

ings

and

Inco

me

by E

mpl

oym

ent C

ateg

ory,

200

1 an

d 20

05

Leve

l in

2001

Leve

l in

2005

% c

hang

e

Non

agric

ultu

reAg

ricul

ture

Non

agric

ultu

reAg

ricul

ture

Non

agric

ultu

reAg

ricul

ture

Wag

e an

d s

alar

y w

orke

rsM

edia

n an

nual

labo

r inc

ome

21,0

64.3

610

,532

.18

20,8

44.0

011

,700

.00

–1.0

511

.09

Med

ian

hour

ly e

arni

ngs

rate

8.32

4.22

8.52

5.06

2.41

20.0

1Lo

w e

arni

ngs

rate

20.5

737

.76

17.6

624

.86

–14.

17–3

4.16

Ind

ivid

ual

sel

f-em

plo

yed

wor

kers

Med

ian

annu

al la

bor i

ncom

e13

,374

.20

6,42

3.42

12,0

00.0

06,

319.

25–1

0.28

–1.6

2M

edia

n ho

urly

ear

ning

s ra

te11

.20

3.88

6.22

—–4

4.43

—Lo

w e

arni

ngs

rate

28.3

255

.96

34.1

351

.59

20.5

0–7

.81

Emp

loye

rsM

edia

n an

nual

labo

r inc

ome

46,8

09.7

09,

298.

0045

,000

.00

31,7

51.1

9–3

.87

241.

48M

edia

n ho

urly

ear

ning

s ra

te26

.87

5.87

19.7

8—

–26.

39—

Low

ear

ning

s ra

te3.

4045

.72

6.83

9.72

100.

85–7

8.74

Hou

seh

old

en

terp

rise

wor

kers

Med

ian

annu

al la

bor i

ncom

e10

,532

.18

5,19

0.74

16,0

53.4

55,

891.

4452

.42

13.5

0M

edia

n ho

urly

ear

ning

s ra

te10

.75

4.12

8.40

——

—Lo

w e

arni

ngs

rate

23.4

064

.36

56.9

557

.70

143.

36–1

0.34

Sour

ce: A

utho

rs’c

alcu

latio

ns w

ith d

ata

from

EM

NV

2001

and

200

5.

Not

e: —

indi

cate

s in

suffi

cien

t or m

issin

g da

ta. M

edia

n an

nual

labo

r inc

ome

refe

rs to

all

the

occu

patio

ns a

nd in

clud

es m

onet

ary,

nonm

onet

ary,

and

in-k

ind

earn

ings

. The

med

ian

hour

ly e

arn-

ings

rate

is c

alcu

late

d fro

m th

e m

ain

occu

patio

n on

ly, e

xcep

t for

the

agric

ultu

ral s

elf-e

mpl

oyed

, agr

icul

tura

l em

ploy

ers,

and

agric

ultu

ral f

amily

ent

erpr

ises,

for w

hich

it is

cal

cula

ted

as p

rofit

spe

r hou

r wor

ked

usin

g th

e ag

ricul

tura

l ent

erpr

ise m

odul

e. In

200

5, th

e ag

ricul

tura

l ent

erpr

ise m

odul

e di

d no

t rep

ort h

ours

wor

ked,

and

ther

efor

e th

e ho

urly

ear

ning

s ra

te c

anno

t be

calc

ulat

-ed

for t

he s

elf-e

mpl

oyed

in a

gric

ultu

re.

agricultural categories, and among agricultural workers the lowest incomeis obtained by household enterprise workers and the individually self-employed. It is also obvious that wages decreased for nonagriculturalemployment while they increased for agricultural workers. The self-employed in nonagricultural work have similar earning rates to the wageemployed, which suggests that wage employment is not necessarily a bet-ter earning option. However, yearly earnings among the self-employed arelower, which suggests that they are employed for shorter periods or workfewer hours.

The first column lists the tiers, meaning the group and subgroup oflabor force categories. The second column shows the number of personsunder each tier for 2001. The third column shows the hierarchical rates,meaning the percentage of people in the subcategory (or tier) for 2001.The fourth and fifth columns show the equivalent numbers and rates for2005. The last column gives the percent change.

The first three tiers (child population, population age 65 and above,and working-age population) illustrate the basic population structure ofthose 6 years of age and older. It is very evident that the share of the pop-ulation between ages 6 and 14 increased its participation among thegroup.This has important implications for the evolution of the labor mar-ket in the coming decade, as the cohort between 6 and 14, which repre-sents the largest fraction of the population, will enter the labor market inthe coming years. The population 65 and older and the working-age pop-ulation reduced their shares in the total population. The table further dis-aggregates the working-age population (15–64) into active and inactive.The rate of inactivity has remained almost constant at 35 percent. Theinactive include the discouraged workers and the seasonally inactive. Theproportion of discouraged workers as a fraction of the active populationhas decreased. But this is also true for the seasonally inactive (from 5 per-cent to 1.6 percent). Among the active population, 96 percent areemployed, with very little change from 2001 to 2005.

The employed population (tier 1.3.2.2) is disaggregated into differentemployment categories. The bulk of the nonagricultural population isemployed as wage and salary workers (43 percent in 2001). In the agri-cultural sector, the bulk of employment is evenly distributed betweenthose employed in agricultural family enterprises and the wage and salaryworkers (11 percent each in 2001).4 There has been little change in thisstructure. Under each employment category, the share of low earners isshown. These are the workers who earn incomes below the poverty line.

Country Context 15

Tabl

e 2.

4H

iera

rchi

cal D

escr

iptio

n of

the

Popu

latio

n Si

x Ye

ars o

f Age

and

Abo

ve, 2

001

and

2005

2001

leve

l H

iera

rchi

cal

2005

leve

l H

iera

rchi

cal

%

(in m

illio

ns)

rate

s(in

mill

ions

)ra

tes

chan

ge

1. To

tal p

opul

atio

n 6

year

s an

d ab

ove

4,15

4,98

310

0.00

4,48

6,70

810

0.00

7.98

1.1

Child

pop

ulat

ion

(6–1

4 ye

ars

of a

ge)

1,03

1,15

824

.82

1,18

5,57

826

.42

14.9

81.

1.1

Child

labo

rers

91,4

438.

8711

0,07

19.

2820

.37

1.2

Popu

latio

n 65

+ y

ears

of a

ge25

7,45

96.

2026

5,74

25.

923.

221.

2.1

Empl

oyed

92,8

1636

.05

97,4

4836

.67

4.99

1.3

Wor

king

age

pop

ulat

ion

(15–

64 y

ears

of a

ge)

2,86

6,36

568

.99

3,03

5,38

767

.65

5.90

1.3.

1 In

activ

ea1,

020,

727

35.6

11,

062,

846

35.0

24.

131.

3.1.

1 D

iscou

rage

d92

,206

9.03

73,7

236.

94–2

0.05

1.3.

2.1

Tem

pora

rily

inac

tive

48,2

924.

7316

,798

1.58

–65.

221.

3.2

Activ

e1,

845,

638

64.3

91,

972,

540

64.9

86.

881.

3.2.

1 U

nem

ploy

ed64

,099

3.47

66,8

373.

394.

271.

3.2.

2 Em

ploy

edb

1,78

1,53

896

.53

1,90

5,70

396

.61

6.97

1.3.

2.2.

1 W

age

and

sala

ried

agric

ultu

re

196,

348

11.0

220

9,39

510

.99

6.64

With

low

ear

ning

s72

,922

37.1

452

,064

24.8

6–2

8.60

1.3.

2.2.

2 W

age

and

sala

ried

nona

gric

ultu

re77

1,31

843

.30

785,

037

41.1

91.

78W

ith lo

w e

arni

ngs

143,

963

18.6

613

8,61

917

.66

–3.7

11.

3.2.

2.3

Indi

vidu

al s

elf-e

mpl

oyed

agr

icul

ture

59,8

543.

3667

,048

3.52

12.0

2W

ith lo

w e

arni

ngs

32,9

5055

.05

33,4

4049

.88

1.49

1.3.

2.2.

4 In

divi

dual

sel

f-em

ploy

ed n

onag

ricul

ture

200,

874

11.2

825

3,12

513

.28

26.0

1W

ith lo

w e

arni

ngs

58,2

0028

.97

86,3

8634

.13

48.4

31.

3.2.

2.5

Empl

oyer

s ag

ricul

ture

35,7

072.

0023

,957

1.26

–32.

91W

ith lo

w e

arni

ngs

13,5

9238

.07

1,31

55.

49–9

0.32

1.3.

2.2.

6 Em

ploy

ers

nona

gric

ultu

re36

,764

2.06

57,4

933.

0256

.38

With

low

ear

ning

s2,

288

6.23

3,92

56.

8371

.51


Country Context 1720

01 le

vel

Hie

rarc

hica

l 20

05 le

vel

Hie

rarc

hica

l %

(in

mill

ions

)ra

tes

(in m

illio

ns)

rate

sCh

ange

1.3.

2.2.

7 In

hou

seho

ld e

nter

prise

s ag

ricul

ture

c19

3,37

710

.85

300,

639

15.7

855

.47

With

low

ear

ning

s79

,136

40.9

294

,246

31.3

519

.09

1.3.

2.2.

8 In

hou

seho

ld e

nter

prise

s no

nagr

icul

ture

c12

2,00

76.

8518

3,94

99.

6550

.77

With

low

ear

ning

s32

,071

26.2

910

4,76

356

.95

226.

661.

3.2.

2.9

Oth

ers

165,

286

9.28

25,0

551.

31–1

69.2

0

Sour

ce: A

utho

rs’c

alcu

latio

ns w

ith d

ata

from

EM

NV

2001

and

200

5.

a. T

he s

urve

y do

es n

ot a

llow

for a

dist

inct

ion

betw

een

disc

oura

ged

wor

kers

who

are

will

ing

to w

ork

and

thos

e w

ho a

re n

ot. T

empo

raril

y in

activ

e w

orke

rs a

re th

ose

wai

ting

to s

tart

a jo

b or

wai

ting

for t

he h

arve

st s

easo

n to

beg

in.

b. E

mpl

oyed

wor

kers

are

sub

clas

sifie

d ac

cord

ing

to th

e m

ain

occu

patio

n. T

he c

ateg

ory

does

not

sum

to to

tal e

mpl

oyed

bec

ause

oth

er e

mpl

oyed

are

cla

ssifi

ed a

s m

embe

rs o

f coo

pera

tive

ente

rpris

es.

c. In

clud

es u

npai

d fa

mily

mem

bers

.

The highest number of low earners can be found among those individu-ally self-employed in agriculture (55 percent have low earnings) andthose that work in household family enterprises in agriculture (40 per-cent have low earnings). Employment in all agricultural categories hasincreased. As will be discussed further, it is unclear how much of thisincrease might be due to errors in the urban-rural weights of the 2001survey.

Finally, table 2.5 shows the sector of employment and level of educa-tion of the employed population. The tertiary sector absorbs most of theemployed population, namely, more than two-thirds of the employed.This share decreased slightly between 2001 and 2005 because of theincrease in employment in the primary and secondary sectors. Finally, thelow level of education of the labor force stands out, as 42 percent of theemployed have an incomplete primary education or less in 2005, and only10 percent have a completed secondary education.

In summary, between 2001 and 2005 Nicaragua’s labor markets saweither no change or very subtle changes in this labor market profile.Perhaps the important events have been the increase in the share of theworking-age population as a fraction of the total population and theincrease in employment in the agricultural sector. In general, as industri-alization progresses, the share of the population in the rural sector tendsto decrease. In very few cases increases in the rural population are seen as


Table 2.5 Other Characteristics of the Employed, 2001 and 2005

Share of Share of total employment total employment

2001 2005

Sector of activity (primary occupation)Primary 18.45 20.60Secondary 11.90 12.56Tertiary 69.64 66.84

Formal schooling attainmentNo school 20.30 17.63Incomplete primary 26.89 24.13Primary 13.72 14.79Incomplete secondary 21.45 22.50Secondary 8.23 10.17Tertiary 9.41 10.78

Source: Authors’calculations with data from EMNV 2001 and 2005.

a response to urban crisis. However, this has not been the case inNicaragua; thus the reason for this increase is yet to be determined. Onepossible explanation is that the population weights used in the 2001 sur-vey were not in accordance with the census behavior of the population(see box 2.1). However, it is not clear to what extent this may be affect-ing the results.

Country Context 19

Box 2.1

Urban versus Rural Population: Possible Data Problems

The table below shows the population calculated from the census and the sur-

veys. According to the surveys, urban population increased substantially from

1998 to 2001, from 54 percent to 58 percent, and then decreased between 2001

and 2005, from 58 percent to 55 percent. It is hard to estimate whether the be-

havior in the surveys is actually true. It is surprising that urbanization increased

substantially and then reversed in such a short time. The available census infor-

mation suggests that there was an increase of 1 percentage point between 1995

and 2005, but there are no data points in between to illustrate the intercensus

behavior. Moreover, the 2001 population estimations used in the 2001 survey

overestimate the population growth. The present report corrects the weights for

this overestimation but makes no adjustments for regional or urban-rural com-

position, as no data were available to do that. It is unlikely that the population

overestimation was uniform across regions or urban and rural populations.

Percentage of regional populationCensus Survey

1995 2005 1998 2001 2005Managua 25.10 24.56 26.06 24.83 24.54

Pacific Urban 17.38 17.13 16.75 17.37 16.95

Pacific Rural 14.16 12.34 15.57 14.34 12.38

Central Urban 10.79 12.21 10.51 12.74 12.28

Central Rural 20.30 19.83 20.82 18.67 19.85

Atlantic Urban 3.89 4.37 5.05 5.50 4.39

Atlantic Rural 8.39 9.56 5.25 6.55 9.62

Overall Urban 54.41 55.92 54.35 58.33 55.83

Labor Regulation in Nicaragua

The largest share of nonlabor costs paid by employers corresponds tosocial security contributions, which amount to 15 percent of the wage.Workers contribute 6.25 percent of their wage for social security.Workersare entitled to one month of paid vacations and an annual bonus that isequivalent to one month of work. They are also entitled to senioritybonuses. In addition to these costs, employers have to pay 2 percent of thetotal payroll for INATEC, the technological training institute. Moreover,there are minimum wages, by sectors, and strong support for unioniza-tion. Firms are allowed to hire temporary workers and can extend thistype of contract indefinitely. The workweek consists of six days and it canbe extended up to 50 hours. Termination of the employment contract isauthorized with no third-party involvement, and workers are entitled toseverance pay upon termination, which varies with tenure.

Nicaragua conducted an enterprise survey for 2003, in conjunctionwith the World Bank. Enterprise surveys collect information among firmsregarding constraints to growth and business activities.5 The informationis often used for the analysis of the investment climate in different coun-tries. Although a complete investment climate assessment is outside thescope of this report, the survey can be used to pinpoint the main bottle-necks that are present and that may be hampering growth and employ-ment generation.

The information collected includes the level of nonwage labor costsand the perception among firms of the rigidity of labor regulation. Usingthis information, the Enterprise Survey Unit at the International FinanceCorporation constructs relative hiring and firing rigidity indexes. Table2.6 compares the results for Nicaragua with other countries in the region(and elsewhere) and with its main trading partners (shown in gray). Ascan be seen, Nicaragua does not appear particularly rigid when comparedwith other countries in the region. In fact, it appears to be one of the leastrigid economies, ranking only below Jamaica and the DominicanRepublic and having an overall performance equal to Chile’s and ElSalvador’s. It is relatively low compared with the United States, one of itsmain trading partners but also the most flexible economy in the world.

Other information collected can be used to asses the main constraintsto investment faced by different firms. Table 2.7 shows the percentage offirms responding that a particular constraint was severely hampering busi-ness functioning and growth. Responses show that labor regulation andthe skills of the labor force are among the least problematic constraints,


and macroeconomic stability and uncertainty and credit issues are severe-ly constraining business functioning and growth.

Minimum wages are set by the Minimum Wage Commission, in whichrepresentatives of the unions, the government, and the private sectornegotiate their levels. Minimum wages are differentiated according to sec-tor of economic activity in an attempt to take into account the level ofeducation of the labor force in each sector (see table 2.8).

Assessing whether the minimum wage is binding in Nicaragua is a hardtask. The fact that there are 12 different minimum wages implies that aresearcher would have to determine separately for each sector of econom-ic activity whether the wage is binding or not, and this would reduce thesample size and thus the reliability of any estimate. This report instead

Country Context 21

Table 2.6 Labor Market Flexibility, Comparative Performance

Difficulty Rigidity Difficulty Rigidity of of hiring of hours of firing employment

Region or economy index index index index

United States 0 0 0 0Jamaica 11 0 0 4Dominica 11 20 20 17Chile 33 20 20 24El Salvador 33 40 0 24Nicaragua 11 60 0 24Colombia 22 40 20 27Uruguay 33 60 0 31Latin America and the Caribbean 34 35 27 32Costa Rica 56 40 0 32OECD 27 45 27 33Guatemala 61 40 0 34South Asia 42 25 38 35Middle East and North Africa 30 45 33 36Honduras 67 40 0 36Mexico 33 40 40 38Brazil 67 60 0 42Dominican Republic 56 40 30 42Sub-Saharan Africa 44 52 45 47Ecuador 44 60 50 51Bolivia 61 60 100 74Venezuela, R. B. de 67 60 100 76

Source: World Bank, Investment Climate Surveys.

Note: The United States is the baseline for comparison; numbers are normalized to zero.

analyzed minimum wages in the four largest sectors in terms of employ-ment: agriculture, manufacturing, commerce, and community services.

A first step in analyzing minimum wages is to plot kernel density esti-mates of wage earnings and explore whether the distribution of earningsdisplays a kink at the minimum wage.

Figure 2.2 illustrates the results for the log of hourly wage earnings for2001, for both formal and informal wages. The vertical line is the corre-sponding log of the hourly minimum wage for the sector. In the case ofthe manufacturing sector, there are three different minimum wages. Thelowest (C$670) corresponds to the manufacturing non-maquila sector.The middle wage (C$895) corresponds to the maquila sector, and thehighest (C$1,010) corresponds to utilities (electricity, gas, and water).

As expected, the distribution of formal wages is to the right of the dis-tribution of informal wages. From the figure, there is some evidence of a


Table 2.7 Issues Affecting the Investment Climate

Percentage of firms answering that it is a

Ranking Investment climate issue very severe problem

1 Corruption 38.32 Cost of credit 34.23 Macroeconomic and regulatory uncertainty 31.44 Access to credit 28.75 Macroeconomic stability 27.06 Noncompetitive practice 26.87 Availability of credit 26.78 Efficiency of justice administration and conflict resolution 19.79 Crime and violence 18.8

10 Transport 17.311 Electricity 17.312 Taxes 14.613 Red tape on taxes 8.414 Property rights 5.515 Skills of the labor force 5.516 Access to land 5.117 Import taxes regulation 4.918 Telecommunications 4.719 Permits and operating licenses 4.420 Labor regulation 3.121 Trade regulation 3.0

Source: Authors’calculations using data from World Bank Enterprises Surveys.

kink around the minimum wage in formal agriculture, while in the infor-mal agricultural sector minimum wages do not seem to have any effect.The maquila minimum wage is binding in manufacturing, but the non-maquila minimum wage seems to be setting the standard for minimumpay in the informal sector, although not in the form of a kink, but ratherby affecting the mode. The effects of minimum wages on commerce areunclear; there seems to be a slight kink for the formal sector, but resultsare sensitive to the assumption about hours worked (calculation assumesa 48-hour workweek). Again, there does not seem to be any effect on theinformal sector.

Minimum wages in the community services sector have no effect oneither formal or informal wages; although both distributions show a kink,it is located at a higher level than the minimum wage. In any case, the dis-tribution of wages does not show important distortions around the mini-mum wage when compared with other Latin American countries.Moreover, there is no evidence of important effects of minimum wageson the informal sector. In many Latin American countries, minimum

Country Context 23

Table 2.8 Minimum Wage and Lowest Wage Paid as a Proportion of Minimum Wage,2001 and 2005C$

Lowest paid wage as a proportion of

Sector Minimum wage minimum wage

2001 2005 2001 2005

Agriculture 542 736 1.23 1.17Fishing — — — —Mining 942 1,377 2.12 1.53Manufacturing 664 988 1.58 1.24Electricity, gas, and water 887 1,242 1.63 1.73Construction 1,001 1,410 1.71 1.32Commerce 1,292 1,752 1.01 0.98Transport and communications 1,001 1,410 1.39 1.22Financial intermediation 1,001 1,410 1.18 1.26Community services 1,110 1,752 0.88 0.70Municipal and central govt. 778 1,066 0.79 0.90

Source: Authors’calculations based on data from the Ministry of Labor and BCN.

Note: — indicates insufficient or missing data. Monthly average minimum wage was calculated as a weighted av-erage of ongoing minimum wages during the year.

wages have been shown to leak to informal markets, suggesting that bothsegments are more integrated than previously thought. This phenomenondoes not seem to occur in Nicaragua, which opens the possibility thatminimum wages in Nicaragua are acting as a barrier to formal job creationand may be contributing to an informal sector that does not comply withminimum wage regulation. The magnitude and importance of this effectmerits further study.


formal informal

(a) agriculture

(c) commerce

(b) manufacturing

(d) community services

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

0

1.0

0.8

0.6

0.4

0.2

0

–4 –2 0 2 4 –4 –2 0 2 4

–4 –2 0 2 4 –4 –2 0 2 4

log hourly earnings rate log hourly earnings rate

log hourly earnings rate log hourly earnings rate

rela

tive

freq

uen

cyre

lati

ve fr

equ

ency

rela

tive

freq

uen

cyre

lati

ve fr

equ

ency

Figure 2.2 Distribution of Wages by Sector and Formality, 2001

Source: Authors’calculations with data from EMNV 2001 and 2005, and BCN.

Note: The vertical line is the corresponding log of the hourly minimum wage for the sector. The manufacturing sec-tor has three different minimum wages.

It is unclear whether the current structure of minimum wages pro-vides much benefit over a unique minimum wage. If the idea of sectoralminimum wages is to take into account the different average skill levelsof the labor force in each sector, it might be better to set a minimumwage by level of education (for the low skilled) rather than by sector.Thecurrent structure of the minimum wage might be introducing unneces-sary distortions into the labor market, and might be segmenting the mar-ket according to skills. This might explain the behavior of maquila facto-ries, which face a higher minimum wage than overall manufacturing and,as a response, may restrict employment to those with a secondary orhigher education. If the analysis assumes that more productive firmshave larger profits and a higher share of skills (as is often the case), thecurrent minimum wage–setting mechanism is acting more as a centralcollective bargaining mechanism to distribute profits between low-skilled workers and firms, rather than as a mechanism for setting thelowest paid wage. But even if this were the objective of having a differ-ential minimum wage by sectors, it is unclear what the advantages of thiscentralized bargaining system would be over a decentralized (firm level)bargaining system.

When firms make hiring decisions they compare the marginal cost oflabor, that is, the minimum wage, with the marginal product, that is, thevalue of output produced by one additional worker. More productivefirms are usually more competitive, account for larger shares of employ-ment, and grow faster. If firms differ in productivity, and minimum wagesare higher for the most productive firms, then low-skilled workers (forwhich minimum wages are binding) will be excluded from the mostdynamic and productive sectors of the economy. Instead, a minimumwage by skill level will mean that more productive firms will have anadvantage with respect to low-productivity firms when hiring low-skilledworkers. Relative to the marginal cost (that is, the minimum wage), themarginal benefit of having an unskilled worker is larger. Therefore, high-productivity firms might be more inclined to increase the use of unskilledlabor (that is, increase the unskilled-labor intensity of the productionprocess), while workers will be equally off (for a given level of education)in any sector or firm. Understanding the employment effects of minimumwages and the impact of its sectoral structure on employment, plus therelative demand for unskilled labor, is beyond the scope of this report. Butit is an area that merits further research.

Country Context 25

Notes

1. The dependency ratio is the ratio of total population to working age popula-tion, and it indicates, on average, how many people a working adult has to sup-port.

2. The value of transformation services corresponds to the difference betweenthe value of imported raw materials and the value of final exports and corre-sponds mostly with the cost of labor and utilities.

3. ILO defines unemployed as those who are not employed and who actuallylooked for a job in the past week.

4. The individually self-employed are the self-employed who do not work withother family members. The employers are those who are self-employed buthave paid workers. The household enterprise workers are the self-employedwho work with unpaid family members or unpaid helpers.

5. The International Finance Corporation of the World Bank Group providescomprehensive data for productivity analysis in emerging markets at itsEnterprise Surveys Web site: http://www.enterprisesurveys.org/.


This chapter describes the labor and productivity profile of growth andlinks it to poverty reduction. It also takes a closer look at the manufactur-ing sector and the maquila sector. The first section describes the maintrends in output, poverty, and employment; the second section decom-poses growth into sectoral employment and productivity changes; thefinal section takes a closer look at manufacturing.

Main Trends in Output

Value added grew at an average annual rate of 4.2 percent between 1998and 2005. Between 1998 and 2001 growth reached 5.42 percent. Growthdecelerated dramatically between 2001 and 2005, reaching an averageannual growth rate of 3.24 percent (table 3.1). Agriculture, construction,and services suffered the largest growth losses. Only transport and thefinancial sector kept their growth pace, but these sectors are small in termsof employment and output. Furthermore, the share of the poor employedin these two sectors is less than 4 percent. Despite this strong decelerationof economic activity, the manufacturing sector managed to grow at anaverage annual rate of 4.4 percent. This has important implications forpoverty reduction, as discussed later. Overall growth was fueled in part by

C H A P T E R 3

Output, Population, Employment, and Poverty

27

reconstruction efforts after Hurricane Mitch struck the country in 1998.These reconstruction efforts meant that the construction sector grew at anaverage of 11 percent per year, although most of this growth was concen-trated in the year after the hurricane, in which construction grew 36 per-cent. Manufacturing, agriculture, and services also registered growth ratesabove the average. Growth in these sectors has important implications forboth employment and poverty, as 60 percent of total employment is con-centrated in these sectors, and 76 percent of the poor earn their livelihoodin these three sectors.

As discussed in chapter 2, between 2001 and 2005, Nicaragua beganto see an important change in the population structure, with the work-ing-age population (between 15 and 64 years of age) increasing its sharein the total population. The working-age population grew at an averageannual rate of 2.7 percent per year, compared with a 1.7 percent averageannual population growth (see figure 3.1).

Main Trends in Population

This population change presents both challenges and opportunities forpoverty reduction. On the one hand, a larger fraction of the populationwill have to find jobs. Between 2001 and 2005 the economy had aninflow of about 350,000 new workers. Had these new workers not beenable to find jobs, poverty would have increased. On the other hand, eachworking adult now has to support fewer dependents, which provides an


Table 3.1 Sectoral Growth, 1998–2005

Average annual growth

1998–2001 2001–05 1998–2005

Agriculture 6.84 2.37 4.26Mining and utilities 5.23 3.00 3.95Manufacturing 5.71 4.42 4.97Construction 11.30 1.11 5.36Commerce, restaurants, and hotels 4.17 3.66 3.88Transport and communications 4.29 4.67 4.51Services 6.22 3.07 4.41Government 1.55 1.67 1.62Financial 8.51 9.76 9.22Total 5.42 3.24 4.17

Source: Authors’calculations based on data from Central Bank of Nicaragua (BCN) and the National Household Living Standards Survey (EMNV).

opportunity for poverty reduction if these new working adults are able tofind sufficiently well-paid jobs.

Furthermore, the cohort ages 10–15, which will have completed thetransition to the working age within the next five years, will imply anadditional 590,000 workers in the labor market.1 Thus the opportunitiesand challenges offered by this population transition will continue to bepresent in the next decade.

Unfortunately, the level of education of this new labor force has notshown much improvement. Although higher primary completion rateswere observed in 2005 compared with 2001, the share of the employedworking-age population with an incomplete secondary education or lessdecreased only 3 percentage points (from 68 percent to 65 percent). Thismeans that each year the share of the employed population with a less-than-complete secondary education decreased by only 1 percent or, equiv-alently, the share of the employed working-age population with a complet-ed secondary education or above increased by 1 percent. At this rate itwould take 23 years to reach a stage at which at least 50 percent of thepopulation had the level of complete secondary education or above.

Nicaragua has one of the lowest education levels in Latin America andCentral America. It ranks only above Guatemala in terms of the education

Output, Population, Employment, and Poverty 29

(a) 2001 (b) 2005

8 6 4 2 0 2 4 6 8 8 6 4 2 0 2 4 6 8

85+80–8475–7970–7465–6960–6455–5950–5445–4940–4435–3930–3425–2920–2415–1910–15

5–1000–04

85+80–8475–7970–7465–6960–6455–5950–5445–4940–4435–3930–3425–2920–2415–1910–15

5–1000–04

female male female male

Figure 3.1 Change in Population Structure, 2001–05

Source: Authors’calculations based on Nicaragua census data

levels of its urban and rural populations (table 3.2). If the population tran-sition is to lead to poverty reduction, two policies will need to be at thefront of the agenda: increasing good employment opportunities and accel-erating educational achievement.

Main Trends in Employment

Using data from household surveys of 2001 and 2001, it is possible to ana-lyze the main trends in employment.All sectors, with the exception of themining and utilities sector and construction, experienced positive employ-ment growth. The average annual total employment growth was 4 per-cent. Moreover, the growth in employment was greater than the growth inthe labor force (3 percent).

Between 2001 and 2005, the growing labor force was absorbed by theagriculture, manufacturing, and commerce sectors.These sectors account-ed for about 67 percent of total employment, and they all experiencedaverage annual growth rates above 2.5 percent, thus accounting for 84percent of total employment growth (table 3.3). On the other hand, com-munity services, which is the other important sector in terms of its


Table 3.2 Average Level of Education of Population Ages 25 to 64

Country Year Urban Rural

Guatemala 2004 6.5 2.4Nicaragua 2001 6.9 3.1Honduras 2003 7.5 3.5Brazil 2005 7.8 3.8El Salvador 2004 8.6 3.8Bolivia 2004 8.9 4.9Venezuela, R. B. de (national total) 2005 8.9 …Dominican Republic 2005 9.1 6.2Mexico 2005 9.6 6.0Costa Rica 2005 9.6 6.8Colombia 2005 9.7 ..Uruguay 2005 9.9 ..Ecuador 2005 10.4 5.6Peru 2003 10.6 5.3Panama 2005 11.1 7.0

Sources: Nicaragua: Authors’calculations based on 2005 survey. Other countries: Economic Commission for LatinAmerica and the Caribbean (CEPAL).

Note: .. indicates negligible value.

employment size, was stagnant, growing at an average annual rate of 1percent. This meant that its contribution to total employment generationwas 5 percent. Figure 3.2 illustrates the sectoral shares of employment for2001 and 2005.

Although commerce absorbed an important fraction of the new laborforce, its growth rate was lower than aggregate employment growth, thuslosing its participation in total employment (see table 3.3). The gain of2.5 percentage points in manufacturing employment and of 1.1 percent-age points in agricultural employment stands out. These sectors arelooked at in more detail later in this chapter.

Main Trends in Poverty

Despite the increase in the working-age population and in the share ofworking-age population who are employed, headcount poverty did notchange (see table 3.4). The number of poor among the working-age pop-ulation, according to the national poverty line, stayed at 46 percent, andthose in extreme poverty stayed at 15 percent. In 2005, poverty in theurban sector was substantially lower than poverty in the rural sector (29percent and 68 percent, respectively).The incidence of poverty (thepoverty gap) decreased a little less than 1 percentage point.


communityservices, 17.26

governmentservices, 2.97

financial services, 2.7

transport,3.93

agriculture, 31.71

mining andutilities, 1.3

manufacturing,11.99

construction, 5.27

commerce,22.87

communityservices, 15.48

governmentservices, 3.15

financial services, 3.05

transport,3.70

agriculture, 32.82

mining andutilities, 0.98

manufacturing,14.51construction,

4.51

commerce,21.79

2001 2005

Figure 3.2 Share of Employment by Sectors, 2001 and 2005

Source: Authors’calculations based on data from BCN and EMNV.

Table 3.4 shows the changes in poverty rates of the working-age popu-lation by area of residence and employment status (from 41 percent to 42percent between 2001 and 2005). It is clear that poverty rates decreasedamong the employed and increased among the unemployed and the inac-tive (defined as discouraged workers and the seasonally unemployed,among others). The increase in poverty among the rural unemployed wasparticularly strong, but the unemployed in the urban sector make up lessthan 1 percent of the total working-age population, so this increase doesnot affect the overall poverty rate in any significant way. Thus, while thepoverty rate among the rural employed decreased, it was more than com-pensated for by an increase in poverty among the rural inactive.

Table 3.5 shows the evolution of employment by sector and povertylevel. The table clearly shows that the poor are overrepresented in agri-culture, and this share may have increased from 2001 to 2005.2 It alsocalls attention to the increase in the share of the poor employed in man-ufacturing, from 8.8 percent to 11 percent, while they are losing theirshare in community services and commerce.

Decomposition of Per Capita Income Growth

The aim of this section is to show how growth is linked to changes inemployment, productivity (output per worker), and population structureat the aggregate level and by sector. The main idea is to profile growth in


Table 3.3 Evolution of Employment by Sectors, 2001 and 2005

Average annual Share of total Change in the share employment employment of total labor force growth (%) generation (%) (percentage points)

Agriculture 4.80 39.5 1.11Mining and utilities –3.25 –1.0 –0.32Manufacturing 8.99 29.8 2.52Construction –0.04 –0.1 –0.76Commerce 2.65 15.2 –1.09Transport 2.35 2.3 –0.23Financial services 7.18 5.2 0.36Government services 5.39 4.2 0.17Community services 1.13 4.8 –1.77Total employment 3.90 100.0 0.02Labor force 2.98

Source: Authors’calculations based on data from EMNV.

per capita value added, to see whether growth has been accompanied byproductivity or employment increases and, if so, in which sectors.

The change in per capita value added between 2001 and 2005 isdecomposed into (i) changes in the demographic composition of the pop-ulation, (ii) changes in productivity, and (iii) changes in the share of work-ing-age population employed. The decomposition is performed at theaggregate level and by sectors. Per capita value added can change fromone year to another if any of these components changes. For example, ifthere is an exogenous increase in productivity (value added per worker)for the same number of workers and for a constant population structure,the higher productivity per worker will imply more value added per per-son. Equally, there might be a change in the structure of the populationso that each working person has fewer dependents; if productivity andemployment do not change, then value added will increase because moreworkers are producing for the same total population. In reality, however,many factors are changing at the same time, so it is difficult to disentan-gle what has happened to each component of per capita value added for


Table 3.4 Headcount Poverty Rates of the Working-Age Population by EmploymentStatus, 2001–05

2001 2005

EmployedRural 63 62Urban 25 25

UnemployedRural 48 61Urban 18 22

InactiveRural 66 69Urban 27 28

Total working ageRural 64 65Urban 26 26Total 41 42

National poverty levelRural 64 68Urban 29 29Total 46 46


a given observed growth. There are several techniques for decomposingand attributing to each component a share of total observed growth. Theresult described used Shapley decompositions, which are described inappendix A in more detail.

Decomposition of per capita value addedTable 3.6 shows the change in value added per capita and in its maincomponents. Per capita value added saw a growth of 7 percent for theperiod, while employment grew almost 17 percent, the population shareof the working age population grew almost 4 percent, and value addedper worker (productivity) decreased about 2 percent.This means that thenew labor force was absorbed by employment, but at a lower productiv-ity level (a lower level of output per worker).

Figure 3.3 illustrates the results for the decomposition at the aggregatelevel. It shows that 74 percent of the change in per capita value added canbe linked to changes in the structure of the population. In other words,had everything else stayed the same, the sole change in the number ofdependents per working-age person would have generated growth equiv-alent to about 74 percent of the actual observed growth (that is, a totalgrowth for the period of 5 percent). Changes in employment were alsoimportant, accounting for some 51 percent of observed growth. Thismeans that if productivity had stayed the same and the number ofdependents per working-age member had also remained constant, the


Table 3.5 Employment by Sector and Poverty Level, Shares of Total Employment,2001 and 2005

Poor Nonpoor Total

2001 2005 2001 2005 2001 2005

Agriculture 53.57 55.66 16.98 17.25 31.71 32.82Mining and utilities 0.95 0.76 1.54 1.13 1.30 0.98Manufacturing 8.85 11.04 14.10 16.89 11.99 14.51Construction 5.09 3.83 5.39 4.98 5.27 4.51Commerce 12.99 11.85 29.53 28.56 22.87 21.79Transport 1.86 1.90 5.33 4.93 3.93 3.70Financial services 1.15 1.03 3.74 4.44 2.70 3.05Government services 1.24 1.40 4.14 4.34 2.97 3.15Community services 14.30 12.54 19.25 17.49 17.26 15.48Total 100.00 100.00 100.00 100.00 100.00 100.00


higher rate of employment would have generated a growth of almost 4percent. Unfortunately, changes in productivity acted in the oppositedirection (–25.99 percent). Had productivity not changed, observedgrowth would have been 9 percent, but decreases in productivity meantthat growth was 1.6 percentage points lower.


Table 3.6 Percentage Change in Selected Variables, 2001–05

% Average annual change growth (%)

Value added 14.47 3.44Value added per capita 7.14 1.74Population 6.85 1.67Population of working age 12.47 2.98Employment 16.54 3.90Employment of working-age population 3.62 0.89Value added per worker –1.78 –0.45


share of working-age population

changes in share of working-age populationemployed

changes in outputper worker

74.35

51.64

–25.99

–40 –20 0 20 40 60 80

contribution to total change (%)

Figure 3.3 Aggregate Employment and Productivity Profile of Growth, 2001–05


Decomposition of changes in output per workerThe key question, then, is why did output per worker decrease? There aremany reasons why output per worker might have decreased. Workersmight have moved to a sector where marginal productivity is lower, totalfactor productivity (TFP) might have decreased, or the capital-to-laborratio may have been reduced as a result of the large inflow of workers intothe economy. These possible alternatives can be explored further bydecomposing the changes in aggregate output per worker into changesthat result from any of these three causes. Figure 3.4 shows the result ofthe decomposition of changes in output per worker for the aggregateeconomy. Total output per worker decreased 1.78 percent. Of thisdecrease, intersectoral employment shifts exerted a positive effect on out-put per worker (half a percentage point) or 6.72 percent of total produc-tivity growth. The capital-to-labor ratio also increased, contributing 1.68percentage points to output per worker; however, TFP suffered an impor-tant reduction, which explains 3.66 percentage points of the decrease inoutput per worker. These data clearly show that TFP changes are respon-sible for the decrease in output per worker.

To understand possible reasons for the observed decrease in TFP, it isimportant to understand that TFP was calculated as a residual (see appen-dix A), which means that it will capture all factors affecting output perworker other than capital and intersectoral shifts. Among the factors thatare likely to affect this residual in an important way are changes in theaverage skill of the labor force, which comprises both experience and edu-cation, and changes in the structure of employment by employment cat-egories (rather than by sectors). Reductions in the skills of the labor forcewould be reflected in lower TFP. Changes in the structure of employ-ment—characterized by increases in the number of workers employed inlow-productivity categories—will also be reflected in lower TFP. Forexample, if an important increase of total employment is concentratedamong family enterprises or the self-employed, which have lower-than-average productivity, then TFP will decrease.

As pointed out in chapter 2 (see table 2.5), the average years of edu-cation of the labor force increased, so the explanation for TFP decreasesdoes not lie on the level of education (at least if quality did not change).Tables 2.3 and 2.4 showed that employment had increased disproportion-ately among household enterprise workers and the individual self-employed, which are the categories with lowest earnings. If householdenterprise workers and the individual self-employed are also those cate-


gories with the lowest productivity (as is most likely the case), then thisincrease in the share of employed in low earning categories might accountfor part of the TFP decrease. New entrants to the labor market also haveless experience and as such may have lower productivities.

An additional explanation for the reduction in TFP might lie in themovement of workers across sectors with different productivities. Forexample, in a segmented labor market, where marginal products differbetween sectors, it is possible that as workers move from low marginalproduct sectors to high marginal product sectors, the average product oflabor falls in the sector where employment rises as marginal decreasingreturns to labor set in. Unfortunately, no data are available for decompos-ing changes in output per worker by sector, to see whether capital-laborratios decreased in the expanding sectors or whether TFP changes explainthese decreases. However, changes in overall productivity and employ-ment by sectors can be examined, as well as intersectoral shifts, to furtherdescribe the aggregate behavior.


total factor productivity

capital-to-labor ratio 1.68

-3.88

0.43intersectoral employment shifts

-5 -4 -3 -2 -1 0 1 2

percentage points

total percentage growth of output per worker = 1.78%

Figure 3.4 Decomposition of Changes in Output per Worker, 2001–05

Source: Authors’calculations based on data from national accounts and EMNV.

Decomposition of intersectoral shiftsIt is possible to understand how changes in the share of employment inthe different sectors help explain the overall contribution of intersectoralshifts to per capita growth. Important literature has found that structuralchange—the movement of labor force shares from low-productivity sec-tors to high-productivity sectors—is an important factor in growth.Increases in the share of employment in sectors with above-average pro-ductivity will increase overall productivity and contribute positively tothe intersectoral shifts. By the same token, movements out of sectors withabove-average productivity will have the opposite effect. Thus, increasesin the share of employment in sectors with below-average productivityshould reduce growth, while reduction in their shares should contributepositively to growth.

Table 3.7 shows the results of decomposing intersectoral shifts usingthe above intuition (see appendix A for details and formulas). The resultsshow that the increasing shares of employment in manufacturing andgovernment explain most of the positive effect of intersectoral shifts andthat movements into agriculture and out of mining and utilities exerted anegative effect on overall per capita growth.

In other words, had employment growth been proportionally distrib-uted among all the sectors, per capita value added growth would havebeen 6 percent lower (that is, the contribution of intersectoral shifts tototal per capita growth). However, because employment growth was dis-proportionately concentrated in manufacturing, a sector with high pro-ductivity, it spurred total growth.

Sectoral decompositionTable 3.8 shows changes in total value added by sector as well as changesin the share of each sector in total value added. All sectors experiencedpositive growth, and overall employment growth was 14.5 percent for thewhole period. Manufacturing, commerce, transport, and “other” saw avalue added growth that was above average and thus gained shares in totalvalue added. The sector referred to as “other” combines community andenterprise services as well as financial services, but the latter has a verysmall share of the total. Agriculture reduced its share. Overall changes inshares were relatively small: manufacturing gained a 1-percentage-pointshare while agriculture lost a 1-percentage-point share.

Table 3.9 shows changes in productivity and employment shares bysectors. All sectors experienced positive employment growth, but growth


was disproportionately concentrated in manufacturing and agriculture.Value added per worker decreased in both manufacturing and agriculture,which were the sectors that experienced the highest increases in employ-ment. It is also worth noting that all sectors that saw an increase inemployment also saw a decrease in productivity, while sectors that expe-rienced an increase in productivity had a decrease in their share ofemployment. There may be several explanations for this. One possible


Table 3.7 Decomposition of Intersectoral Shifts

Direction of employment Contribution to

Sectoral contributions share in shift intersectoral shifts (%)

Agriculture Movements into –84.72Mining and utilities Movements out of –161.67Manufacturing Movements into 279.11Construction Movements out of 6.39Commerce Movements out of 46.14Transport Movements out of –48.24Government Movements into 49.52Other Movements out of 13.48Total contribution of intersectoral shifts 100.00

Source: Authors’calculations using data from national accounts and EMNV.

Table 3.8 Sectoral Growth, 2001–05

Share of total value added

Total value added growth 2001–05 2001 2005 % change

Agriculture 9.80 22.1 21.2 –4.08Manufacturing 18.91 19.0 19.8 3.87Mining and utilities 12.55 3.6 3.5 –1.69Construction 4.52 4.9 4.5 –8.69Commerce 15.45 18.2 18.3 0.85Transport 20.03 7.1 7.4 4.85Government 6.86 7.0 6.6 –6.65Other 18.39 18.1 18.7 3.42Total 14.47 100.0 100.0

Source: Authors’calculations based on data from BCN.

explanation is that changes in employment mainly capture new entrantsto the labor market and these new entrants have lower productivity thanmore experienced workers. The sectors that have a stronger increase inemployment growth absorb most of this new labor force and thus have astronger negative effect on average productivity. Alternatively, asexplained above, the inflow of workers into these sectors implied a lowercapital-labor ratio, as decreasing marginal returns to labor set.

Decreases in productivity and increases in employment were concen-trated in agriculture and manufacturing. Increases in the relative size ofthe manufacturing sector (in terms of employment) account for animportant share of growth. Table 3.10 illustrates the contribution of eachsector to total per capita value added growth for 2001–05. Sectoral con-tributions are decomposed into the (i) contribution of changes in outputper worker, (ii) contribution of the sector to employment rate growth,and (iii) contribution of the sector to the intersectoral shift component oftotal changes in output per worker (see appendix A for details). Overall,manufacturing, commerce, transport, and other services contributed pos-itively to growth, while agriculture, mining and utilities, construction, andgovernment had a negative contribution.

Despite the enormous employment growth in manufacturing, thedecrease in output per worker was so large that it more than offset the

Table 3.9 Employment Shares and Productivity, by Sectors of Economic Activity,2001–05

Absolute 2001 2005 % change 2001 2005 change

Agriculture 10,973 9,988 –8.97 19.21 20.60 1.39Manufacturing 25,032 21,097 –15.72 7.26 9.11 1.85Mining and utilities 43,097 55,364 28.47 0.79 0.62 –0.17Construction 14,701 15,393 4.70 3.19 2.83 –0.36Commerce 12,505 13,005 4.00 13.86 13.68 –0.18Transport 28,418 31,084 9.38 2.38 2.32 –0.06Government 37,223 32,239 –13.39 1.80 1.98 0.17Other 14,308 15,643 9.33 12.09 11.64 –0.45Total 15,757 15,477 –1.78 60.59 62.78 2.19



Employment/working-age population (%)Output per worker (1994 C$)

employment growth. However, shifts of labor into manufacturing andaway from other sectors of lower productivity more than compensatedfor this effect, so that in the aggregate, manufacturing contributed 12 per-cent of total per capita growth. Other services accounted for a significant14 percent of observed growth of output per capita, and commerce andtransport contributed with 9 percent and 5 percent of the growth, respec-tively. In all three sectors the effect was mostly due to increases in outputper worker.

Agriculture, on the other hand, contributed negatively to per capitagrowth, with two different effects. First, it saw a decrease in output perworker; second, it increased its share of total employment. Given that ithas below-average productivity, this shift toward agriculture reducedgrowth.

These results suggest that, had productivity in agriculture and manu-facturing not decreased, then value added per worker would have been 5percentage points higher.The next section tries to provide an understand-ing of what was happening in manufacturing. A closer look at the agricul-tural sector will be undertaken in a later section.

Table 3.10 Total Sectoral Contribution to Growth, 2001–05

Contribution Contribution Contributions of changes to total of in output employment intersectoral

per worker rate changes shifts Total Sectoral contributions (%) (%) (%) (%)

Agriculture –29.53 32.78 –5.30 –2.05Mining and utilities 13.03 –4.11 –10.12 –1.20Manufacturing –48.47 43.48 17.48 12.49Construction 3.14 –8.44 0.40 –4.90Commerce 10.39 –4.23 2.89 9.05Transport 9.46 –1.37 –3.02 5.08Government –14.19 4.11 3.10 –6.97Other 23.89 –10.59 0.84 14.14Subtotals –32.26 51.64 6.26 25.64

Demographic component — — — 74.36

Total 100.00

Total % change in output per capita 2001–05 7.14


Note: — insufficient data.


The behavior of wagesThe effects of changes in employment and productivty on povertydepend on how they affect earnings. Table 3.11 presents the medianwages by sector of economic activity, calculated from the household sur-veys.The table shows that, in real terms, wages in agriculture increased 17percent while wages in manufacturing decreased 2 percent between 2001and 2005. As will be seen, lower value added per worker in agriculturewas compensated by higher producer prices, which may have preventedthe falls in productivity from translating into lower wages. In the case ofthe manufacturing sector, wages did decrease, but much less than produc-tivity, so that productivity falls were not totally passed on to wages.

A Closer Look at the Manufacturing Sector

Table 3.12 shows employment generation by subsector. In an analysis ofwhich sectors absorbed most of the employment generation, 66 percentof the employment growth was in the food and beverage sector and theclothing sector. The tobacco sector contributed to an additional 8 percentof employment generation. The last row of the table shows employmentgeneration by maquila, which contributed an amazing 32 percent ofemployment generation between 2001 and 2005, most of which wasclothing (see box 3.1).

Table 3.13 shows the median wages for these sectors. The two sectorsthat generated most of the employment growth in manufacturing (food

Table 3.11 Wages by Sector of Economic Activity, 2001 and 2005

Median wage C$ 2001

2001 2005 Real growth (%)

Agriculture 6,840 8,018 17.2Mining and utilities 20,000 23,495 17.5Manufacturing 12,600 12,364 –1.9Construction 10,080 10,000 –0.8Commerce 13,329 13,364 0.3Transport 18,900 18,327 –3.0Financial services 18,175 21,238 16.9Government services 23,665 25,833 9.2Community services 12,179 14,118 15.9



and clothing) saw differing behavior. In the food sector, median wagesdecreased, while wages in the clothing sector increased.

Another way of looking at the types of jobs generated is to see whichtype of employment showed more growth. In Nicaragua the lowestincome is observed in those working in household family enterprises, fol-lowed by the individual self-employed, while the highest income corre-sponds to employers, followed by waged and salaried workers. Table 3.14shows that 32 percent of the employment generated in manufacturingwas concentrated in family enterprise workers, 17 percent was in the indi-vidual self-employed, and 55 percent was in the waged and salaried cate-gories. This means that 48 percent of the jobs created in manufacturingwere low income.

It is worth noting that the maquila sector may have played an impor-tant role in counteracting the negative effect of the growing number ofemployed in family enterprises and the decreasing wages in the food


Table 3.12 Employment Generation by Subsector, 2001 and 2005

Employment Share of growth Employment total

Total number (number of growth employment of employed new jobs) (%) generation

2001 2005 2001–05 2001–05 2001–05

Food and beverage 69,754 100,398 30,644 43.93 38.54Tobacco 1,118 7,394 6,276 561.35 7.89Textiles 4,305 6,472 2,167 50.34 2.73Clothing 62,811 84,989 22,178 35.31 27.89Wood products 8,766 10,571 1,805 20.59 2.27Paper and prints 2,455 5,210 2,755 112.25 3.47Petroleum 469 622 153 32.52 0.19Chemicals 1,752 2,926 1,174 67.02 1.48Plastic and rubber 1,441 1,362 –79 –5.46 –0.10Other nonmetallic mineral 10,917 13,461 2,544 23.31 3.20

productsMetal and metal products 14,597 16,460 1,864 12.77 2.34Machinery and equipment 1,945 3,695 1,750 89.99 2.20Transport equipment 443 634 190 42.96 0.24Other 15,271 21,355 6,084 39.84 7.65Total manufacturing 196,043 275,549 79,506 40.56 100.00

Maquila employment 35,565 61,000 25,435 71.52 31.99


sector. No data are available on wages for the maquila sector, but most ofthat sector is concentrated in the clothing sector, which saw an overallwage increase of 17 percent. If the maquila sector participated in thiswage increase, then it may have had important positive effects on incomegeneration, since this sector contributed to 32 percent of total employ-ment (see box 3.1)


Table 3.13 Wages in the Manufacturing Sector, 2001 and 2005

Median annual income (C$ 2001)

2001 2005 Real growth (%)

Food and beverage 13,800 12,364 –10.4

Tobacco 16,960 9,848 –41.9

Textiles 9,960 8,379 –15.9

Clothing 11,495 13,424 16.8

Wood products 11,700 11,455 –2.1

Paper and prints 19,840 14,773 –25.5

Chemicals 30,805 20,606 –33.1

Plastic and rubber 16,350 18,038 10.3Other nonmetallic mineral products 10,032 11,882 18.4Metal and metal products 11,970 15,273 27.6Machinery and equipment 7,036 16,743 138.0Other 4,944 17,438 252.7


Table 3.14 Employment Generation in Manufacturing by Type of Employment, 2001 and 2005

Employment Share growth Employment of total

Total number (number of growth employment of employed new jobs) (%) generation

2001 2005 2001–05 2001–05 2001–05

Waged and salaried workers 127,748 171,355 43,607 34.14 54.85Individual self-employed 33,153 46,837 13,683 41.27 17.21Employers 12,443 10,850 –1,592 –12.80 –2.00 Family enterprise workers 22,277 47,526 25,249 113.34 31.76Other 421 — – 421 — –0.53Total 196,043 276,569 80,526 41.08 101.28


Note: — insufficient data.


The main picture that emerges from this chapter is as follows. Growthwas mainly concentrated in the manufacturing sector. Employmentgrowth was concentrated in both manufacturing and agriculture.Unfortunately, this growth had a limited impact on the income opportu-nities of the poor. On the one hand, despite the fact that wages in agri-culture increased, returns in this sector still offer the lowest income gen-eration, so that growing employment in this sector is not likely to reducepoverty. On the other hand, employment generated in manufacturing wasdivided evenly between family enterprise employment and wage employ-ment. Family enterprise employment has a very low income-generationpotential. Wage employment has a better potential, but its benefits to thepoor seem to have been limited by two main factors: (i) wages decreasedin the food sector, which saw most of the employment growth in manu-facturing; and (ii) clothing, which was the other important sector in terms

Box 3.1

Evolution of the Maquila Sector and Its Importance in the Employment Growth in Manufacturing

The first maquila factories started in 1990, with the establishment of the first pub-

licly owned export processing zone (EPZ), Las Mercedes. In 1994 the EPZ law was

reformed to allow private ownership by both foreign and domestic investors and

an expansion of EPZ to other regions in the country, with the particular aim of

providing employment opportunities for poorer areas. Currently, there is the

zone of Las Mercedes. The rest are distributed in 30 industrial parks, which are pri-

vate EPZs. The main investors are from Taiwan, the United States, the Republic of

Korea, Nicaragua, Italy, Honduras, Belize, and Mexico.

Initially, EPZ firms were not allowed to use domestic raw materials, which lim-

ited their spillover effects to those of employment generation, but with the sign-

ing of the Central American Free Trade Agreement, these restrictions were re-

moved. EPZs have a 100 percent exemption on corporate income tax for the first

15 years of operation. They are exempt from capital gains on real estate; all cor-

porate taxes; excise, sales, and municipal taxes; and import duties on machinery,

inputs, and equipment. Currently there are 84 firms, of which the large majority

are in the clothing sector.

(continued)


Box 3.1

(continued)

In 2006 there were 68,300 employees in the EPZs, which corresponds to just

over 3 percent of total employment, 18 percent of formal employment, and 64

percent of formal employment in manufacturing. This employment is mostly fe-

male (90 percent). EPZ firms have to comply with all labor regulations. There is a

special minimum wage for the EPZs, which is above the manufacturing mini-

mum wage. The rationale for this is that labor force quality in the EPZ is higher

than that in the average manufacturing labor force. Apparently, to be a worker in

the maquila sector, employees have to have completed secondary education,

which substantially reduces access to this employment for the very poor. The

fact that the minimum wage is higher for the maquila sector may contribute to

this selection.

For 2005, maquila exports represented 50 percent of total exports, and the

value of transformation services (which corresponds to the sum of wages, utili-

ties, and services paid) was equivalent to 24 percent of value added in manufac-

turing. The graph below illustrates the evolution of employment and output in

the maquila sector.

exp

ort

s U

S$ 1

,00

0

emp

loym

ent

90,000

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

0

maquila sector employment and output

1994 1998199719961995 1999 2000 20052004200320022001 2006

exports US$ 1,000

employment

of job creation, offered limited employment access for the poor becauseof its skill requirements (secondary education or above).

Notes

1. This increase is net of those ages 60–64 who will be exiting the labor market.

2. The Household Survey for 2001 (EMNV 2001) shows a rural share of thepopulation that is inconsistent with the census. According to the survey, theshare of rural population increased between 2001 and 2005.The census showsthe opposite. Apparently the survey of 2001 underestimated the rural popula-tion. If this is the case, the increase in the share of employed in agriculturemight be exclusively due to the under-representation of rural households inthe survey of 2001.


Annex 3ADecomposition of Per Capita Value Added Growth

Step 1: Decomposing aggregate growthA simple way of understanding how growth has translated into increasesin productivity and employment at the aggregate level and by sectors (orregions) is to perform a simple decomposition of growth in per capitaGDP. To do so, we use the fact that per capita GDP, Y/N = y can beexpressed as:

or:

where Y is total value added, E is total employment, A is the total popu-lation of working age, and N is total population. In this way, Y/E = istotal output per worker, E/A is the share of working-age population (i.e.,the labor force) employed, and A/N is the labor force as a fraction of totalpopulation.

Thus, changes in per capita value added can be decomposed intochanges in output per worker, changes in employment rates, and changesin the size of the labor force. Using Shapley decompositions, total changesin per capita value added will be equal to:

The first term in the summation will be the contribution of changes inoutput per worker, the second term the contribution of changes in theemployment rate, and the third term the contribution to changes in thedemographic component.

With this information we can present aggregate growth in terms ofeach of these components, where:


YN

YE

EA

AN

=

y e a= ω * *

∆ ∆ye a e a e a e at t t t t t t t=

++

+⎡ = = = = = = = =ω 1 1 0 0 1 0 0 1

3 6⎣⎣⎢

⎤

⎦⎥

++

++= = = = = =∆e

a a at t t t t tω ω ω ω1 1 0 0 1 0

3tt t

t t t t

a

ae e

= =

= = = =

⎡

⎣⎢

⎤

⎦⎥

++

+

0 1

1 1 0 0

6

3∆

ω ω ω tt t t te e= = = =+⎡

⎣⎢

⎤

⎦⎥

1 0 0 1

6ω

will be the fraction of growth that can be linked to changes in output perworker,

will be the fraction of growth that can be linked to changes in theemployment rate, and

will be the fraction of growth that can be linked to changes in the shareof total population that is of working age, and where the bar denotes thefraction of growth explained by the component. In this way percentagegrowth between two periods can be expressed as follows:

Once we have decomposed aggregate employment growth we can gofurther and understand, first, the role played by different sectors inchanges in employment, and second, the role of capital, total factor pro-ductivity (TFP), and intersectoral shifts in explaining changes in outputper worker, both at the aggregate level and by sectors. This amounts todoing a stepwise decomposition, first decomposing aggregate growth intoemployment and productivity changes, and then decomposing employ-ment and productivity changes by sectors.

Step 2: Understanding which sectors contributed most to employment generationTo understand which sectors contributed to most of the employmentgeneration, we can further decompose employment growth (e) by sec-tors. The easiest is of course to express the total growth in employmentas the sum of employment generation in each sector, as follows:


ω ω≡+

++⎡

⎣= = = = = = = =∆

e a e a e a e at t t t t t t t1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

e ea a a at t t t t t t t≡

++

+⎡

⎣= = = = = = = =∆

ω ω ω ω1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

a ae e e et t t t t t t t≡

++

+⎡

⎣= = = = = = = =∆

ω ω ω ω1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

∆ ∆ ∆ ∆yy

yy

eyy

ayy

= + +ω

∆ ∆e eii

s

==∑

1

where

is just the change in employment in sector i as a share of total working-age population. Let

denote the fraction of the aggregate employment rate change that can belinked to changes in employment in sector i. The supraindex e will makeexplicit that it is the contribution to employment growth (as opposed tototal per capita growth).

Step 3: Decomposing changes in output per worker by sectors and between and within componentsWe can further decompose output per worker into sectoral employmentshifts and changes in output per worker by sectors by noting that:

or equivalently:

where Yi is value added of sector i = 1…S, Ei is employment in sector i,and E is total employment. This means that

will correspond to output per worker in sector i, and

is the share of sector i in total employment. This equation just states thatchanges in output per worker are the weighted sum of changes in outputper worker in all sectors, where the weights are simply the employmentshare of each sector.


∆ ∆eEAi

i=

e e ei

ei≡∆ ∆/

sEEi

i=

ω ii

i

YE

=

ω ω==∑ i ii

S

s1

YE

YE

EE

i

iS

i= ∑

Using the Shapley approach, changes in aggregate output per workercan be decomposed as:

Each term

is the change in output per worker that can be linked to changes in out-put per worker in sector s. The last term in the equation, B, is thechange in output per worker due to intersectoral employment changes(i.e., between sectors). That is, employment movement from low-pro-ductivity sectors to high-productivity sectors should increase total out-put per worker, and the flows from high-productivity sectors to low-pro-ductivity sectors should reduce aggregate output per worker. If this lastterm is negative, the reallocation of employment by sectors was detri-mental to overall productivity growth. Finally, the term W correspondsto total changes in output per worker net of relocation effects (this com-ponent is also referred to as the “within” component in the decomposi-tion literature).

We can then denote the fraction of aggregate output per workergrowth that can be linked to growth in output per worker in sector i as

where again the bar denotes the fact that we are referring to contribu-tions, and the supraindex denotes the fact that it is a contribution toaggregate output per worker growth , rather than a contribution to out-put per capita growth y.


∆ ∆ ∆ω ω ω=+⎛

⎝⎜⎞

⎠⎟+

+= = =1

1 0 1 12

2 0 2

2* *, , , ,s s s st t t t == = =⎛

⎝⎜⎞

⎠⎟+ +

+⎛

⎝⎜⎞

⎠⎟1 0 1

2 2... * , ,∆

∆

ω

ω

ii t i ts s

w

* ,+ ∆siiω tt i t

i

S

B

= =

=

+⎛

⎝⎜⎞

⎠⎟∑ 0 1

1 2

ω

ω

,

∆

∆ω ii t i ts s

* , ,= =+⎛

⎝⎜⎞

⎠⎟0 1

2

ω ω ωωi i

i t i ts s≡

+⎛

⎝⎜⎞

⎠⎟= =∆ ∆* /, ,0 1

2

Similarly, we can define the contribution of within-sector productivitygrowth as

and the contribution of intersectoral shifts as

Step 4: Understanding the sources of changes in output per worker(net of intersectoral shifts) at the aggregate level and by sectorsThe terms and i will capture changes in output per worker, but theirinterpretation is not so straightforward. Increases in output per workercan come from three different sources: (i) increases in the capital-laborratio, (ii) increases in TFP, and (iii) relocation of jobs from bad jobs sec-tors (low productivity) to good jobs sectors (high productivity). To seethe first two sources, note that under constant returns to scale, ifYt=t f (Et,Kt), where Kt is the capital stock and t is a technologicalparameter also know as total factor productivity, then output per workercan be expressed as Yt/Et=t f (l, Kt/Et). Therefore, output per worker,Yt/Et, will depend on changes in capital-labor ratio and in TFP growth.Note that it may also capture cyclical behavior of output: firms operatingin economic downturns may have underutilized capital, so when thedemand rises again it will be reflected as rise in output per worker. Thethird source is simply the result of workers moving from a low-produc-tivity sector (or firm) to a high-productivity sector (or firm), so that in theaggregate, average output per worker will rise. From step 3 we found thatit is possible to isolate the effect of intersectoral shifts. The term w isjust changes in output per worker net of intersectoral shifts.

If data on capital stock are available, then we can assume a particularfunctional form for the production function and separate the contribu-tion of higher capital-labor ratios and TFP. For example, if we are willingto assume that the production function is Cobb-Douglas, then:


YE

KE

= ⎛⎝⎜

⎞⎠⎟

−

Φ1 α

ω ω ωωB B≡∆ ∆/

ω ω ωωw w≡∆ ∆/

In competitive markets, 1- is the share of payments to capital in totalvalue added. It is usually available from national accounts data or, if thereare enough time series, then it can be estimated by taking logs and esti-mating:

where t is an (optional) time trend capturing technological change and Ìis a residual. Once we have a value of ·, we can proceed to decomposechanges in output per worker, net of intersectoral shifts, into changes inTFP and changes in the capital-labor ratio.

Once we have an estimate of ·, we can calculate TFP as a residual. Inthe first period it will be:

To calculate TFP in the second period, however, we need to take intoaccount that part of the change in output per worker that was a conse-quence of relocation shifts. This means that TFP in the second period hasto be calculated as:

The term in square brackets is just output per worker in the secondperiod (t = 1) net of relocation effects.

Once we have calculated TFP for both periods, we are able to calcu-late whether changes in output per worker net of relocation effects arethe result of increases in capital per worker or in TFP. To do so we can usethe following formula:

where k is simply the capital-labor ratio. The first term in the right-handside is the contribution of changes in the capital-labor ratio to growth inoutput per worker net of relocation effects, and the second term is thecontribution of changes in TFP.


YE

KE

TFPt t

t

⎛⎝⎜

⎞⎠⎟

⎛⎝⎜

⎞⎠⎟

== =

−

=0 0

1

0/( )α

YE

KE

TFPt

Bt

t

⎛⎝⎜

⎞⎠⎟

−⎡

⎣⎢

⎤

⎦⎥

⎛⎝⎜

⎞⎠⎟

== =

−

1 1

1

∆ωα

/( )

==1

∆ ∆ ∆ω αα α

wt t tk

TFP TFPTFP

k k=

++

+− = =−

=−

1 0 11

01

2( ) ( tt =1

2)

ln ln ( )lnYE

KE

t= + − ⎛⎝⎜

⎞⎠⎟

+ +Φ 1 α µ

This means that changes in total output per worker can be expressedas the sum of changes in TFP, changes in the capita-labor ratio, and inter-sectoral shifts:

As before, let

denote the share of output per worker that can be linked to changes inthe capital labor ratio,

denote the share of growth in output per worker that can be linked toTFP changes, and

denote the share of changes in output per worker that can be attributedto intersectoral employment shifts.

Step 5: Understanding the role of each sector on intersectoral shiftsIt is possible to understand further how changes in the share of employ-ment in the different sectors help explain the overall contribution ofintersectoral shifts to per capita growth. Important literature has foundthat structural change, which is movements of labor force shares fromlow-productivity sectors to high-productivity sectors, is an important fac-tor behind growth. Increases in the share of employment in sectors withabove-average productivity will increase overall productivity and con-tribute positively to the intersectoral shift term that captures the reloca-tion effects, B. On the contrary, movements out of sectors with above-average productivity will have the opposite effect. By the same token,increases in the share of employment in sectors with below-average pro-ductivity should reduce growth, while a reduction in the share of employ-ment in sectors with below-average productivity should contribute posi-tively to growth.


∆ ∆ ∆ω αα α

=+

++− = =

−=

−

kTFP TFP

TFPk kt t t t1 0 1

10

1

2( ) ( == +1

2)

∆

∆

ω

ω

w

B

ω ω ωωB B≡∆ ∆/

k kTFP TFPt tω α ω≡

+− = =∆ ∆1 0 1

2( )

/

TFP TFPk kt t

ωα α

ω≡+−

=−

=∆ ∆( )

/1

01

1

2

Using the above intuition, we can rewrite the intersectoral shift as:

The term in parentheses is the difference between a sector i’s produc-tivity (averaged between the two periods),

and the average productivity of all the economy (averaged over the twoperiods; note there is no sectoral subindex),

Therefore, the contribution of sector i to the intersectoral shifts term willbe:

Thus, if sector i has productivity bellow the average productivity, andincreases its share si, its contribution will be positive, that is outflowsfrom this low productivity sector have contributed to increase output perworker. If on the other hand, if the sector sees an increase in its share,these inflows into this low productivity sector will decrease output perworker and thus have a negative effect on the intersectoral shift term.Themagnitude of the effect will be proportional to: i) the difference I the sec-tor’s productivity with respect to the average and ii) the magnitude of theemployment shift.

As before, we can denote the share of intersectoral shift that isexplained by sector i as:


∆ ∆ωω ω ω ω

B ii t i t t t

i

S

s=+

−+⎛

⎝⎜⎞

⎠⎟= = = =

=∑ , ,0 1 0 1

1 2 2

ω ωi t i t, ,= =+0 1

2

ω ωt t= =+0 1

2

∆sii t i t t t

ω ω ω ω, ,= = = =+

−+⎛

⎝⎜⎞

⎠⎟0 1 0 1

2 2

s si ii t i t t t

BBω ω ω ω ω

ω=+

−+⎛

⎝⎜⎞

⎠⎟= = = =∆ ∆, , /0 1 0 1

2 2

Step 6: Putting everything togetherOnce the above steps are completed, the percent contribution of eachfactor to total changes in GDP per capita can be obtained as follows:

Contribution of demographic shifts

As in step 1

Aggregate changes in output per worker

As in step 1

Contribution of changes in the employment rate

As in step 1

Contribution of increases in sectoral employment

It is calculated as the contribution of changes in employment in sector ito total employment rate changes (step 2), times the contribution ofemployment rate changes to changes in total GDP per capita (step 1)


a ae e e et t t t t t t t≡

++

+⎡

⎣= = = = = = = =∆

ω ω ω ω1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

ω ω≡+

++⎡

⎣= = = = = = = =∆

e a e a e a e at t t t t t t t1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

e ea a a at t t t t t t t≡

++

+⎡

⎣= = = = = = = =∆

ω ω ω ω1 1 0 0 1 0 0 1

3 6⎢⎢⎤

⎦⎥ / ∆y

e e e

e e e

ie

i

i=

= ⎡⎣ ⎤⎦

*

/ *∆ ∆

Contribution of changes in output per worker within sectors

It is the contribution of within changes in output per worker to totalchanges in output per worker (step 3) times the contribution of aggre-gate output per worker to GDP per capita (step 1)

Contribution of intersectoral employment shifts

It is the contribution of between changes in output per worker to totalchanges in output per worker (step 3) times the contribution of aggre-gate output per worker to GDP per capita (step 1)

Within changes in output per worker in sector i

It is the contribution of sector i to within changes to total changes inoutput per worker (step 3) times the contribution of output per workerto changes in per capita GDP (step 1)


ω ω ω

ω

ωw w

ii t i t

i

S s s

=

=+⎛

⎝⎜⎞

⎠⎟⎛

⎝⎜

⎞

⎠= =

=∑

*

, ,*∆ 0 1

1 2 ⎟⎟⎡

⎣⎢⎢

⎤

⎦⎥⎥

/ *∆ω ω

ω ω ω

ω ωω

ωB B

ii t i t

i

S

s

=

=+⎛

⎝⎜⎞

⎠⎟⎡

⎣

= =

=∑

*

, ,* /∆ ∆0 1

1 2⎢⎢⎢

⎤

⎦⎥⎥*ω

ω ω ω

ω ω

ωi i

ii t i ts s

=

=+⎛

⎝⎜⎞

⎠⎟⎛

⎝⎜

⎞

⎠⎟

= =

*

* / *, ,∆ ∆0 1

2ωω

Contribution of shifts in the share of employment witnessed by sector i

It is the contribution of sector i to the between component of changesin output per worker (step 5) times the contribution of the betweenemployment shifts component to total GDP per capita (calculated asabove, in number 6)

Contribution of TFP (net of intersectoral shifts)

It is the contribution of TFP growth to changes in output per workernet of intersectoral shifts (step 4) times the contribution of withinchanges in output per worker to total GDP (calculated as above innumber 5)

Contribution of capital-labor ratio

It is the contribution of changes in the capital-labor ratio to changes inoutput per worker net of intersectoral shifts (step 4) times the contribu-tion of within changes in output per worker to total GDP (calculated asabove in number 5)


k k

kTFP TFP

w

t t w

=

=+⎡

⎣⎢

⎤

⎦⎥

− = =

ω

α

ω

ω ω

*

( )/ *∆ ∆1 0 1

2

TFP TFP

TFPk k

w

t t

=

=+⎡

⎣⎢

⎤

⎦⎥

−=

−=

ω

α α

ω

ω

*

( )/∆ ∆

10

11

2**ωw

s s

s

i i B

ii t i t t t

B=

=+

−+⎛

⎝⎜⎞= = = =

ω ω

ω ω ω ω

*

, ,∆ 0 1 0 1

2 2 ⎠⎠⎟⎡

⎣⎢⎢

⎤

⎦⎥⎥

/ *∆ω ωB B

Some background knowledge of how labor income and its componentsaffect household poverty is useful to an understanding of what the prior-ity policies should be. A labor profile of the population should informpolicy makers as to how households are distributed among sectors, whattheir status in employment is, and what the determinants of per capitahousehold labor income are. This can be done by dividing the populationinto the poor and the nonpoor—defined according to national and inter-national poverty lines—or by using income quintiles. Another method forunderstanding how labor markets have affected household welfare is todisentangle the sources of labor income growth that are responsible forthe observed changes in total labor income.

This chapter is structured as follows: the first section sketches a laborprofile of the population; the second focuses on the decomposition ofhousehold labor income growth through the use of the panel componentof the 2001 and 2005 surveys; the final section is concerned with the agri-culture sector.

C H A P T E R 4

Employment and Labor IncomeProfile of the Population

59

Income and Employment Profile

Table 4.1 and table 4.2 show the employment status of the working-agepopulation by quintile and poverty level. There has been an increase intotal working-age population among the poor, which means that there arefewer dependent people within a household and there are potentiallybetter employment and income opportunities for the household as awhole. Among poor households, not all the members of working agelooked for a job (the inactive members increased from 39 percent in 2001to 40 percent in 2005), but most of those who sought a job actually foundone. The proportion of unemployed remained almost constant, while thenumber of employed increased by almost 1 percentage point.

The 2001 household survey asks the reasons for inactivity.Discouraged workers represent 11 percent among the poor and 8 percentamong the nonpoor. Those who are temporarily inactive (who have occa-sional jobs, are waiting for the harvest season, or are waiting to start a newjob) correspond to 3 percent of the inactive among the poor and 2 per-cent among the nonpoor. The largest shares of the inactive are homemak-ers and students: 52 percent and 34 percent are homemakers among thepoor and the nonpoor, respectively; 14 percent and 32 percent are study-ing (among the poor and nonpoor, respectively).

Benefits from formal employment among the poor and nonpoorTable 4.3 and table 4.4 describe the structure of employment by quintileand poverty level. The fraction employed in each category is shown as aproportion of employed individuals. As discussed above, wage employ-ment in the formal sector is very small; only 17 percent of the employedhave formal jobs, most of which are held by the nonpoor (14 percent).This close relationship between poverty and formal employment does nothave an immediate interpretation. Either poverty is a consequence of thelack of formal employment, or being poor hampers access to formalemployment. Alternatively, both informal employment and poverty maybe a consequence of lack of education and skills. It would be importantto look further into this relationship, to assess the importance of generat-ing formal employment in relation to removing barriers to employmentmobility among the poor, including access to education.

The number of wage workers employed in the informal sectordecreased by 7.8 percent. What is more important is that the decreasewas even greater among the poor than among the nonpoor (–13 percentcompared with –3.78 percent). The individual self-employed with no


61

Tabl

e 4.

1Em

ploy

men

t Sta

tus o

f the

Wor

king

-Age

Pop

ulat

ion

by Q

uint

ile,

2001

and

200

5

Q1

Q2

Q3

Q4

Q5

Tota

l

2001

2005

2001

2005

2001

2005

2001

2005

2001

2005

2001

2005

Empl

oyed

9.3

10.4

10.4

11.6

12.4

12.2

13.6

13.4

16.4

15.2

62.2

62.8

Une

mpl

oyed

0.2

0.2

0.2

0.3

0.5

0.4

0.5

0.7

0.8

0.6

2.2

2.2

Inac

tive

6.1

6.7

6.5

6.9

6.6

7.4

8.2

6.6

8.2

7.4

35.6

35.0

Tota

l15

.617

.317

.118

.719

.620

.122

.320

.725

.523

.210

010

0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

Nat

iona

l Hou

seho

ld L

ivin

g St

anda

rds

Surv

ey (E

MN

V).

Tabl

e 4.

2Em

ploy

men

t Sta

tus o

f the

Wor

king

-Age

Pop

ulat

ion

by P

over

ty L

evel

, 200

1 an

d 20

05

Poor

Non

poor

Tota

l

2001

2005

2001

2005

2001

2005

Empl

oyed

59.7

60.6

61.2

64.4

60.6

62.8

Une

mpl

oyed

1.3

1.5

3.0

2.7

2.3

2.2

Inac

tive

38.9

37.9

35.8

32.9

37.1

35.0

Tota

l10

0.0

100.

010

0.0

100.

010

0.0

100.

0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

Tabl

e 4.

3Em

ploy

men

t Cat

egor

ies b

y Q

uint

ile, 2

001

and

2005

Q1

Q2

Q3

Q4

Q5

Tota

l

2001

2005

2001

2005

2001

2005

2001

2005

2001

2005

2001

2005

Wag

ed e

mpl

oyed

priv

ate

form

al s

ecto

r0.

50.

61.

41.

52.

82.

74.

65.

07.

67.

716

.917

.4W

aged

em

ploy

ed p

rivat

e in

form

al s

ecto

r6.

45.

46.

66.

28.

27.

26.

76.

96.

56.

334

.532

.0W

aged

em

ploy

ed p

ublic

sec

tor

0.1

0.2

0.3

0.2

0.4

0.3

0.9

0.9

1.5

1.7

3.1

3.4

Self-

empl

oyed

with

no

paid

em

ploy

ees

2.5

2.2

3.2

3.3

3.8

3.6

4.2

4.4

5.1

5.0

18.7

18.5

Empl

oyer

s w

ith p

aid

empl

oyee

s0.

30.

10.

80.

40.

80.

61.

31.

32.

42.

65.

75.

1Fa

mily

ent

erpr

ise w

orke

rs5.

16.

04.

54.

74.

04.

94.

43.

83.

24.

321

.223

.6To

tal

14.9

14.5

16.7

16.2

20.0

19.4

22.0

22.3

26.3

27.7

100.

010

0.0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

Tabl

e 4.

4Em

ploy

men

t Cat

egor

ies b

y Po

vert

y Le

vel,

2001

and

200

5

Poor

Non

poor

Extr

emel

y Po

orTo

tal

2001

2005

2001

2005

2001

2005

2001

2005

Wag

ed e

mpl

oyed

priv

ate

form

al s

ecto

r2.

52.

714

.414

.70.

20.

416

.917

.4W

aged

em

ploy

ed p

rivat

e in

form

al s

ecto

r15

.313

.519

.218

.54.

84.

134

.532

.0W

aged

em

ploy

ed p

ublic

sec

tor

0.4

0.5

2.6

2.9

0.1

0.2

3.1

3.4

Self-

empl

oyed

with

no

paid

em

ploy

ees

6.9

6.4

11.8

12.1

1.9

1.4

18.7

18.5

Empl

oyer

s w

ith p

aid

empl

oyee

s1.

40.

64.

24.

40.

20.

05.

75.

1Fa

mily

ent

erpr

ise w

orke

rs10

.712

.110

.511

.53.

94.

321

.223

.6To

tal

37.3

35.9

62.7

64.1

11.1

10.4

100.

010

0.0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

62

paid employees and also employers with paid employees decreased theirshare among the poor, while the number of family enterprises—oftenassociated with low income generation—rose by about 1 percentagepoint for both the poor and the nonpoor. The number of poor employedin the public sector remained almost constant.

Increases in public transfers and remittancesPublic transfers increased for the poor, while remittances increased forthe entire population (but less so for the poor). Table 4.5 and table 4.6show the earnings profile of the population by quintile and by povertylevel. No significant differences are evident in the income structure of thepopulation. For all levels of income, wage employment is the main sourceof income, accounting for a little over 45 percent of total income. Themain difference is that poor wage earners receive their earnings from agri-culture. This share decreased slightly for all income levels as remittancesand public transfers increased. Remittances for the nonpoor showed thelargest increase, while public transfers showed the greatest increase forthe poor, and among them, for the extremely poor. It is worth noting thatin 2001 public transfers were regressive in the sense that the poorreceived fewer transfers as a proportion of their total income. In 2005 thissituation changed and public transfers became progressive.

Decomposition of Changes in Labor Income

A traditional way to understand how labor markets have affected welfareis to disentangle the sources of household per capita labor income that areresponsible for observed growth or decreases in average householdincome.1 Per capita household labor income—that is, the total incomethat the household earns from labor divided by the number of membersin the household—can change for several reasons: because income peremployed member increases, because unemployment decreases, becausethe number of members that actively participate in the labor market rises,or because the dependency rate decreases.

This section discusses the main source of per capita labor incomegrowth between 2001 and 2005. The panel component of the survey isused to decompose, for each household, how much of the change in laborincome was attributed to changes in each of the components mentionedabove (see appendix B for the methodology). In addition, the method dif-ferentiates between four types of employment: wage work in agriculture,

Employment and Labor Income Profile of the Population 63

64

Tabl

e 4.

5St

ruct

ure

of In

com

e by

Qui

ntile

, 200

1 an

d 20

05

Q1

Q2

Q3

Q4

Q5

Inco

me

sour

ce20

0120

0520

0120

0520

0120

0520

0120

0520

0120

05

Wag

e em

ploy

men

t agr

icul

ture

29.2

26.0

15.1

14.5

12.7

9.1

5.9

5.5

3.0

3.3

Wag

e em

ploy

men

t non

agric

ultu

re18

.115

.029

.126

.735

.432

.337

.036

.740

.034

.1Se

lf-em

ploy

men

t agr

icul

ture

22.3

27.6

17.3

21.4

13.7

14.3

8.5

8.6

3.6

5.6

Self-

empl

oym

ent n

onag

ricul

ture

8.8

7.2

16.4

12.5

17.0

16.7

21.9

21.4

25.2

22.1

Publ

ic tr

ansf

ers

1.9

6.8

0.8

5.4

1.6

4.4

2.6

3.1

2.3

3.0

Fam

ily re

mitt

ance

s (in

tern

al)

3.6

3.3

4.0

3.3

2.1

3.5

4.3

3.9

2.6

3.4

Fam

ily re

mitt

ance

s (e

xter

nal)

0.9

1.4

1.3

2.6

2.6

4.2

2.6

4.9

4.4

8.8

Oth

er n

onla

bor s

ourc

es15

.312

.715

.913

.715

.015

.617

.115

.918

.919

.7To

tal

100.

010

0.0

100.

010

0.0

100.

010

0.0

100.

010

0.0

100.

010

0.0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

Tabl

e 4.

6St

ruct

ure

of In

com

e by

Pov

erty

Lev

el, 2

001

and

2005

Poor

Non

poor

Extr

emel

y Po

or

Inco

me

sour

ce20

0120

0520

0120

0520

0120

05

Wag

e em

ploy

men

t agr

icul

ture

20.7

18.6

5.9

5.0

30.8

28.2

Wag

e em

ploy

men

t non

agr

icul

ture

24.6

22.2

38.6

35.1

16.4

14.0

Self-

empl

oym

ent a

gric

ultu

re19

.023

.27.

38.

222

.227

.4Se

lf-em

ploy

men

t non

agric

ultu

re13

.810

.522

.121

.18.

76.

4Pu

blic

tran

sfer

s1.

46.

02.

33.

21.

86.

6Fa

mily

rem

ittan

ces

(inte

rnal

)3.

63.

33.

13.

64.

03.

4Fa

mily

rem

ittan

ces

(ext

erna

l)1.

42.

43.

46.

40.

91.

2O

ther

non

labo

r sou

rces

15.6

13.7

17.4

17.5

15.2

12.6

Tota

l10

0.0

100.

010

0.0

100.

010

0.0

100.

0

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

wage work in nonagriculture, self-employment in agriculture, and self-employment in nonagriculture. This means that, in addition to the shareof income growth that was due to increases in employment, the discus-sion can include (i) whether this employment growth took place in anyof the employment categories mentioned above and (ii) which categorieshad increases in earnings (income per employed member).

The decomposition uses a sample of 1,250 households whose mem-bers are classified according to their occupation (waged and salariedworkers versus the self-employed) and their sector of employment (agri-culture and nonagriculture).2 Labor income growth is decomposed intofour main terms: income in sector j to total employment, the employ-ment rate (which is equal to one minus the unemployment rate), theactivity rate, and the share of working-age people within a household.Furthermore, the first component is disaggregated into four subterms,which represent productivity gains and employment shares in each sectorof employment. Table 4.7 presents the labor profile of the population bypoverty level.

Several features are worth noting. First, wage employment in nonagri-culture is the highest earnings option in agriculture for both the poor andthe nonpoor. It should also be noted that the nonpoor have a lower earn-ings rate than the poor in agriculture, most likely because the nonpoor

Table 4.7 Labor Profile of the Population by Poverty Level, 2001 and 2005

Poor Nonpoor

2001 2005 2001 2005

Average annual labor income per worker (C$, 2001)Employed in wage work, agriculture 2,104 2,220 1,079 1,264Employed in wage work, nonagriculture 6,235 6,227 21,476 18,219Self-employed in agriculture 5,898 6,032 2,942 3,279Self-employed in nonagriculture 3,133 3,840 12,552 12,152

Share of employment (%)Waged employed in agriculture 11.2 12.4 2.5 2.9Waged employed in nonagriculture 31.9 31.7 57.8 54.8Self-employed in agriculture 41.0 39.8 9.2 9.9Self-employed in nonagriculture 15.9 16.0 30.1 31.7

Employment rate (%) 98.4 98.0 96.5 96.1Activity rate (%) 63.1 65.0 66.5 68.9Share of working-age members within a household (%) 52.9 59.0 64.2 67.1Average per capita labor income, annual (C$, 2001) 3,588 4,522 9,989 10,085



work fewer hours in agricultural activities, not because they earn less perhour. Employment rates are very high among both the poor and the non-poor, but they are slightly higher for the poor, which merely reflects thefact that they cannot afford to be unemployed. Conversely, participationrates are slightly higher for the nonpoor, and dependency rates are substan-tially higher for the poor: for 2005 the poor showed 59 percent of theirmembers of working age; among the nonpoor this ratio was 67 percent.

There have been some important changes in the labor profile for theyears analyzed. First, dependency rates among the poor decreased sub-stantially, even more than for the nonpoor. Second, there was an impor-tant increase in income per worker in self-employment for both agricul-ture and nonagriculture. On the other hand, the share of the employed ineach employment category remained almost constant, with a slightincrease in waged agricultural employment.

Table 4.8 shows by quintile the same labor profile as in table 4.7,which permits a clearer understanding of labor profiles and their changesamong the poor and nonpoor households. Two important phenomenastand out. First, for the poorest 20 percent, income from nonagricultur-al wage employment is not the highest earnings option, whereas for allother quintiles it is. For the poorest 20 percent, income from self-employment is the best earnings option. Two different effects might beresponsible for this: the poor might work fewer hours as wage employ-ees in nonagriculture, and the poor might earn less per hour worked. Forthe poorest 20 percent, the highest earnings option is self-employmentin agriculture. The second phenomenon is the increase in income for thevery poor households.

Table 4.9 shows the average change in per capita household income byquintile. Between 2001 and 2005 the poor benefited more from econom-ic growth because their labor income grew substantially more than laborincome for the other groups. For the poorest quintile the annual per capi-ta labor income growth rate was 14 percent. It was about 5 percent in thesecond quintile, and it became negative in the last quintile (–1 percent).It is interesting to note that agriculture was the sector in which the poor,both the waged and salaried workers and the self-employed, saw theirincome decreasing, while the income of the poor working in other sectorsshowed a substantial increase. Despite the important growth in the percapita income of the lowest quintile, it was not sufficient to bring themabove the poverty line. In 2001 the poorest 20 percent had an average percapita income of C$2,609; the 14 percent increase still left it well belowthe C$5,241 of the poverty line. Figure 4.1 illustrates this growth.


67

Tabl

e 4.

8La

bor P

rofil

e of

the

Popu

latio

n by

Qui

ntile

, 200

1 an

d 20

05

Q1

Q2

Q3

Q4

Q5

2001

2005

2001

2005

2001

2005

2001

2005

2001

2005

Ave

rag

e an

nu

al la

bor

inco

me

per

wor

ker (

C$,

2001

)W

aged

agr

icul

tura

l em

ploy

men

t3,

057

2,74

21,

716

1,77

31,

243

1,90

445

897

01,

474

1,24

2W

aged

non

agric

ultu

ral e

mpl

oym

ent

3,84

13,

633

7,86

47,

328

9,30

310

,551

15,4

9514

,482

33,6

8324

,955

Agric

ultu

ral s

elf-e

mpl

oym

ent

5,17

56,

878

6,62

36,

107

4,32

14,

621

2,79

23,

859

2,57

91,

955

Non

agric

ultu

ral s

elf-e

mpl

oym

ent

1,51

11,

536

3,56

43,

929

5,78

97,

043

10,2

2111

,459

18,7

2315

,809

Shar

e of

em

plo

yed

(%)

Wag

ed a

gric

ultu

ral e

mpl

oym

ent

15.6

18.1

9.8

9.1

4.8

6.7

1.4

2.8

2.2

1.4

Wag

ed n

onag

ricul

tura

l em

ploy

men

t20

.622

.539

.033

.649

.447

.656

.853

.660

.659

.2Ag

ricul

tura

l sel

f-em

ploy

men

t53

.349

.436

.738

.620

.922

.28.

69.

13.

54.

2N

onag

ricul

tura

l sel

f-em

ploy

men

t10

.69.

814

.518

.724

.423

.533

.034

.433

.233

.5

Empl

oym

ent r

ate

(%)

98.4

97.8

98.2

98.1

97.2

96.8

96.5

97.3

96.5

95.0

Activ

ity ra

te (%

)63

.964

.661

.265

.565

.665

.865

.969

.667

.769

.7Sh

are

of w

orki

ng-a

ge m

embe

rs w

ithin

a h

ouse

hold

(%)

49.0

55.9

55.2

59.5

55.9

62.8

62.4

65.7

70.7

70.8

Aver

age

per c

apita

labo

r inc

ome,

ann

ual (

C$, 2

001)

2,59

83,

459

4,02

25,

041

4,70

55,

977

6,97

88,

201

15,9

0613

,696

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

data

from

EM

NV.

Finally, the decomposition results are shown in table 4.10. The tableshows the contribution of each component to the observed change in percapita labor income by quintile. Two factors were most important in rais-ing the income of the poorest 20 percent of the population: (i) theobserved increase in income per employed worker in agricultural self-employment (44 percent of the total increase in per capita householdincome), and (ii) the important increase in the share of working-age peo-


Table 4.9 Per Capita Household Income Changes, by Quintile, 2001–05

Annual growth rate Level of per capita of per capita household income

Quintile household income (%) (C$ 2001)

1 14.22 2,608.902 5.36 3,844.403 8.71 4,862.364 6.98 7,015.595 –1.00 14,897.57


Note: 2005 poverty line in C$, 2001: 5,241

16

14

12

10

8

6

4

2

0

-21 2 3 4 5

Figure 4.1 Growth in Average Per Capita Income, by Quintile, 2001


Note: The data reflect the panel component of the survey.

ple within a household (38 percent of the observed change in per capitahousehold income). Participation rates also made an important contribu-tion (11 percent).

For the second quintile the main source of income growth came fromlower dependency rates and higher participation rates. The larger fractionof employed household members in nonagricultural wage jobs also con-tributed to the higher labor income.

Decreases in the number of dependents per working age person wereseen in all but the richest 20 percent, and they were an important oppor-tunity for poverty reduction. In all but the richest 20 percent, participa-tion rates also increased, and in all but the middle quintile employmentrates increased, contributing positively to poverty reduction.3

It should be noted that increases in agricultural wages for the panelsample seem to be relatively small compared with the wage increasesseen for the whole sample. It is unclear from this exercise whether agri-cultural wages may have played a more important role in reducing theincidence of poverty.

A Closer Look at Agriculture

Household survey data show a sizable increase in real per capita laborincome for the self-employed in agriculture, which points to a need tounderstand where this gain came from: was it driven by relative prices,quantities, or productivity?

Data from the Food and Agriculture Organization of the UnitedNations (FAO) and the Central Bank of Nicaragua (BCN) can be used todisentangle the effect of each component of the increase in agriculturalproduction, which represents 26 percent of total Nicaraguan production.A useful measure is goods produced by the poor (precisely, by the self-employed among the poor), even if none of them could be considered ascontributing to a large proportion of the GDP or generating a large num-ber of jobs. In addition, the analysis considers the behavior of exportproducts that might have affected agricultural wages. The products con-sidered are beans, coffee, beef, milk, rice, and corn. The first two items areexport goods: beans and coffee represented 8.5 percent and 35.5 percent,respectively, of agricultural GDP in 2001. The rest are considered “sensi-tive” goods, according to the definition used by the Monge-González,


Castro-Leal, Saavedra Gutiérrez (2004). The report states that sensitivegoods “are those with high tariff protection, are economically vulnerableand possess significant socio-economic importance.” This means that theyare produced by small and medium-scale farmers (see table 4.11). Beefproduction represents the largest share of GDP (4 percent in 2001), andthe production of white corn generates the largest number of jobs(175,000), or 9 percent of total employment. Figure 4.2 to figure 4.5 lookat productivity (yields), area harvested, and relative producer prices.

As for productivity changes, figure 4.2 shows the evolution of absoluteproductivity measured as yield per hectare. Productivity remained con-stant for the period under analysis for beans, coffee, and milk, while riceand maize saw increases in productivity of about 13 percent.

The United States and some Central American countries (Costa Rica,El Salvador, and Honduras) are the main trade partners of Nicaragua, sothis analysis compares relative productivity (in relation to U.S. productiv-ity) across these countries. For all products analyzed (except for drybeans), Nicaragua seems to be the least efficient country among the fourcountries in figure 4.3. In some cases, relative productivity, measured asyield per hectare of cultivated land with respect to U.S. productivity,decreased over the 15-year period analyzed, as well as over the years of


Table 4.10 Shapley Decomposition of Per Capita Labor Income, by Quintile

Q1 Q2 Q3 Q4 Q1

Income per waged worker in agriculture –0.90 –17.10 –1.55 1.29 –3.30Share of employed in waged agriculture 5.15 –2.44 –1.17 2.76 –9.80Income per waged worker in nonagriculture 3.45 19.13 33.21 –3.69 289.27Share employed in waged nonagriculture 2.70 –32.69 3.02 –8.71 77.70Income per self-employed worker in 44.64 –5.14 –14.72 –3.39 55.61

agricultureShare of self-employed agriculture –14.00 3.38 –7.47 –4.90 –20.84Income per worker self-employed in 10.29 –20.86 3.28 30.82 136.31

nonagricultureShare of self-employed in nonagriculture –0.96 14.07 6.76 1.05 –31.38Employment rate 0.53 4.50 –3.74 5.46 4.00Participation rate 11.14 63.99 16.40 42.49 –342.18Inverse of dependency 37.94 73.17 65.96 36.83 –55.39Total 100 100 100 100 100


Note: Table shows the percentage share of the contribution of each component to the observed change in percapita labor.

the surveys (2001–05). Relative productivity actually decreased for threeproducts out of a total of four (figure 4.3), the exception being coffee.The relative gains in productivity were modest.

Despite these low levels of productivity and the decreases in relativeproductivity, the observed gains in absolute productivity for maize andrice may have helped the small farmers of these products.


Table 4.11 Number of Farms, by Sensitive Product, according to Farm Size, 2001

Product Small Medium Large Total

Rice 6,714 4,873 5,742 17,329Corn 58,378 53,087 29,919 141,384Milk 64,855 26,391 5,718 96,964Beef 64,855.00 26,391.00 5718 96,964.00

Sources: Nicaraguan Agriculture and Central American Free Trade Agreement (CAFTA) 2004; Monge-González, Cas-tro-Leal, Saavedra Gutiérrez, 2004.

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

year

yiel

d (k

g/h

a)

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

beans (incl. cow peas), dry coffee, greenmaize milk, whole, freshrice, paddy

Figure 4.2 Productivity of Sensitive Products by Yield per Hectare, 1990–2005

Source: Authors’calculations based on FAO data.

Note: Because survey data refer to 2001 and 2005, the line is used to highlight the first year of survey data.

Between 2001 and 2005, the area harvested increased for three of theproducts analyzed (milk, maize, and beans), while for the others the arearemained relatively constant (figure 4.4). Given that land productivityremained almost constant, this was the main source of the productionincreases seen for all of the goods considered (figure 4.5). In the case ofmaize, the increase in output was close to 30 percent, while for the otherproducts it was less than 15 percent. In any case, and despite the impor-


1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0yi

eld

(kg

/ha)

year1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

yiel

d (k

g/h

a)

year1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

01990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

yiel

d (k

g/h

a)

year1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

yiel

d (k

g/h

a)

year1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

01990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Nicaragua Costa RicoEl Salvador Honduras

(a) milk

(c) coffee

(b) dry beans

(d) rice

Figure 4.3 Relative Productivity by Product, 1990–2005

Source: Authors’calculations based on data from FAO.

Note: Graphs report Nicaraguan productivity relative to U.S. productivity.

tant increases in output, it is unlikely that aggregate output growth wasabove the observed employment growth of 21 percent for the wholeperiod, which would explain the decrease in value added per workerreported in chapter 2.

In regard to prices, the pattern of producer price indexes for three bas-kets of goods as they are computed by the Central Bank of Nicaragua ispresented.The three aggregates are cereals, export goods, and meat.A sig-nificant price increase for all the products over the survey years can beobserved (figure 4.6 to figure 4.9). Looking at the producer prices of eachsingle good shows an increase for all the goods considered, except for milk(according to the Central Bank data).

This increase in producers’ prices suggests that the terms of tradeimproved for agricultural producers, as the producer prices increasedmore than the overall consumer price index (CPI). The case of export


1,000

900

800

700

600

500

400

300

200

100

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

year

hec

tare

s (t

ho

usa

nd

s)

beans (incl. cow peas), dry coffee, greenmaize milk, whole, freshrice, paddy

Figure 4.4 Area Harvested for Sensitive Products, 1990–2005


goods (figure 4.6) deserves special attention; after 1999, the basis year,the producer price index dropped dramatically. This may be attributed tothe 2000 crisis in coffee prices in the world market that affected the priceof green coffee, which is the producer price of coffee but not the price ofcoffee in grains.

Thus, it seems that the gains made by the self-employed in agriculturebetween 2001 and 2005 are due to the evolution of the terms of trade(that is, relative prices). Increases in the area harvested were importantbut were probably not sufficient to keep value added per worker fromfalling in response to the apparent inflow of workers to agriculture. Risesin agricultural production may also explain the increases in agriculturalwages seen for the overall sample.


700

600

500

400

300

200

100

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

year

tho

usa

nd

s to

nn

es

beans (incl. cow peas), dry beefcoffee, green maizemilk, whole, fresh rice, paddy

Figure 4.5 Production Volume for Sensitive Products, 1990–2005


The observed decrease in productivity (for two out of a total of fourgoods) relative to the United States poses a considerable challenge forNicaragua with respect to its main trade patterns. Nicaragua needs invest-ments in order to recover productivity and to fill the gap with the maintrade partners. This is particularly important, because income for ruralhouseholds appears to be tied to price variations, which increases the vul-nerability of this population to price shocks.


1.2

1.0

0.8

0.6

0.4

0.2

01999 2000 2001 2002 2003 2004 2005 2006

year

pro

du

cer

pri

ce in

dex

/CP

I

Figure 4.6 Producer Prices Relative to Consumer Prices for Export Goods,1999–2006


1.4

1.2

1.0

0.8

0.6

0.4

0.2

01999 2000 2001 2002 2003 2004 2005 2006

year

pro

du

cer

pri

ce in

dex

/CP

I

Figure 4.7 Relative Prices of Trade for Meat, 1999–2006



1.4

1.2

1.0

0.8

0.6

0.4

01999 2000 2001 2002 2003 2004 2005 2006

year

pro

du

cer

pri

ce in

dex

/CP

I

Figure 4.8 Relative Prices for Cereals, 1999–2006


250

200

150

100

50

01999 2000 2001 2002 2003 2004 2005 2006

year

pro

du

cer

pri

ce in

dex

/CP

I

coffee, green rice, paddybeans, dry meatmilk, whole, fresh maize

Figure 4.9 Relative Prices for Sensitive Products, 2001–06


Notes

1. See Kakwani, Neri, and Son (2006) for an application of this decomposition tothe analysis of pro-poor rates of growth.

2. Some methodological clarifications are important. First, people of working ageare selected by dropping child laborers and the working elderly. This mightimply overestimating productivity and underestimating employment sharesand thus their contribution to total labor income growth. This is not a seriousissue as they represent less than 2 percent and 7 percent, respectively, of thetotal working population (average over the two years of the survey). Second,a little over one-third of the sample is dropped in both 2001 and 2005 becausethere is no correspondence between workers and reported income withinhouseholds. Some households report income in a sector where no one isemployed and vice versa. It might be an issue of misreported income for someof the households. Additionally, in many cases it seems that many peoplereporting earnings from agricultural self-employment are actually receivingrents from farms and are not directly employed in agriculture. Because this isnot labor income but rents, these households are dropped from the sample.Finally, the analysis drops a small number of households that report a jump inthe employment rate from 0 to 1, as they have an analogous increase in incomeand therefore a growth rate of income that goes to infinity. The selection iscompletely neutral across quintiles because it drops proportionally more poorthan rich households as they are more likely to suffer from misreportedincome in agricultural business.

3. An interpretation is risky given the small number of households in each quin-tile. For these estimations the analysis excluded the 2.5 percent in the tails ofchanges in total labor income, and results change when these outliers areincluded. Additionally, the sample selected shows no increase in wages exceptfor the third and fourth quintiles, and agricultural wage increases are lowerthan for the whole sample.


Annex 4ADecomposition of Labor Income Growth

The labor income profile is best described at the household level. A sim-ple and useful characterization of households in terms of labor indicatorscan be obtained by noting that the average labor income of household jcan be written as (borrowing from Kakwani, Neri, and Son 2006):

(A–1)

where is the total labor income of household j; Hj is the total hoursworked by working-age members of household, j; Ej is the total numberof employed in the household; Lj is the number of participants in thelabor market; and Aj is the number of working-age members. In this way

=IL/H corresponds to average earnings per hour worked, h = H/E cor-responds to average hours worked, E/L is the employment rate, l = L/A isthe participation rate, and a = A/N is the ratio of working-age membersto total household members, or the dependency rate. For simplicity, letthe above equation be rewritten as:

(A–2)

where (1 – uj) corresponds to the employment rate of household j, whichcan be rewritten as 1 minus the household’s unemployment rate uj. Notethat (omega bar) is different from (simple omega) which refers tooutput per worker in the decomposition of GDP per capita.

In many contexts there is an important fraction of child laborers andelderly workers, and calculating earnings per hour worked by theemployed of working age is overestimating real household productivity.In these cases, it might be better to abstract from the structure of thehousehold according to working age (Aj in equation A1) and calculatedependency rates as the number of participating individuals over the totalof working household members (Aj/Lj). Define Ej as the number of work-ing individuals irrespective of whether they are of working age or not anddefine hours worked, Hj, as total hours worked for all employed individ-uals irrespective of age.


I

N

I

H

H

E

E

L

L

A

A

NjL

j

jL

j

j

j

j

j

j

j

j

j

=

ι ϖjL

j j j j jh u l a= −( )1

I jL

ϖ

ϖ

By averaging each of the components of the household’s per capitalabor income over subgroups of population, we can obtain a full profileof labor market characteristics. For example, if we divide households byquintile of income, it will describe the average labor market characteris-tics of each quintile. Let denote the subset of households belonging toa particular quintile. It is possible to compare deciles by average depend-ency rates,

,

average participation rates,

,

average hours worked,

,

incidence of unemployment,

,

and earnings per hour worked

.

A traditional way to understand how labor markets have affected wel-fare is to disentangle the sources of labor income growth that are respon-sible for observed changes in total labor income.1

From equation A2, the average per capita labor income of the subset of households (whether poor or nonpoor households, or householdsfalling within an income range or with particular demographic character-istics) will then be:

(A–3)


1N

ajjΩ Ω∈∑

1N

ljjΩ Ω∈∑

1N

hjjΩ Ω∈∑

1N

ujjΩ Ω∈∑

1N j

jΩ Ω

ϖ∈∑

1 11

N N

h u

jL

j

j jj

jjj

Ω Ω Ω

Ω Ωln

ln ln ln( )

ιϖ

∈

∈ ∈∑∑ ∑

=+ + −

∈∈

∈ ∈

∑∑ ∑+ +

⎛

⎝

⎜⎜⎜

⎞

⎠

Ω

Ω Ω

ln lnl ajj

jj

⎟⎟⎟⎟

It is thus possible to decompose the change in the average per capitahousehold labor income of group into changes in its different compo-nents: changes in average log earnings per hour worked, changes in aver-age of log hours worked, changes in average log unemployment rates, andso forth. In particular:

(A–4)

In this way we can easily see whether growth in the average laborincome of the poor (or any group ) was due to changes in employmentrates, participation rates, hours of work, or earnings per hour worked.

We can go a step further and decompose average earnings per hourinto earnings per hour from self-employment (j) and earnings per hourfrom wage employment (wj):

,

with corresponding to the share of wage employment in total hoursworked and corresponding to the share of self-employment. In thiscase, however, log-linearization of equation A2 is no longer possible, andwe would have to perform Shapley decompositions to analyze incomechanges.

Comparing changes in average incomes of the poor, and their compo-nents, with changes in average incomes of the nonpoor can shed some lighton what the channels are through which a growth process is affecting theincome of the poor. In many cases, however, there might be considerableheterogeneity among employment sectors. In these cases it is useful to per-form these decompositions by disaggregating households according toother characteristics: for example, dividing households depending on theirmain occupation (e.g., differentiating between rural farmers, rural nonfarmworkers, sector of occupation of household head, and so forth).

Note

1. See Kakwani, Neri, and Son (2006) for an application of this decomposition tothe analysis if pro-poor rates of growth.


∆ ∆ ∆Ω Ω Ω Ω ΩΩ

1 1 1N N N

hjL

jj j

jj

ln ln lnι ϖ∈ ∈∈∑ ∑∑= +

ln( )+ − +∈∑∆ ∆

Ω Ω Ω

11

1N

uNj

j

lln lnlN

ajj

jj∈ ∈

∑ ∑+Ω Ω Ω

∆ 1

ϖ ππjL

jw

j j jh w h= +

hjw

hjπ

81

Ann

ex 4

BEs

timat

ion

Resu

lts

Tabl

e 4B

.1M

ean

and

Stan

dard

Dev

iatio

n, b

y Em

ploy

men

t Cat

egor

y

Wag

e W

orke

rsEm

ploy

ers

Self-

Empl

oyed

Hou

seho

ld E

nter

prise

s

Varia

ble

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Earn

ings

6.07

313

.240

29.2

9348

.983

16.3

6715

.787

8.07

115

.039

Age

31.2

4432

.716

42.3

2042

.627

36.3

6739

.438

44.7

0844

.896

Year

s of

edu

catio

n3.

121

8.40

73.

339

8.02

72.

654

5.37

51.

729

4.03

9G

ende

r0.

916

0.53

70.

949

0.78

10.

885

0.39

30.

930

0.69

6N

o. o

f chi

ldre

n <

6 y

ears

1.37

00.

878

0.97

60.

740

1.32

30.

904

1.31

90.

927

No.

of c

hild

ren

ages

7–1

51.

502

1.29

61.

506

1.11

81.

281

1.34

62.

276

1.55

6N

umbe

r of a

dults

3.32

63.

549

3.50

93.

299

2.87

93.

146

3.58

93.

326

Num

ber o

f eld

erly

0.11

60.

200

0.12

70.

183

0.16

90.

133

0.07

20.

109

Non

labo

r inc

ome

3.21

010

.529

5.85

715

.076

3.65

38.

364

4.48

17.

597

Man

agua

0.08

20.

431

0.00

00.

312

0.04

60.

383

0.00

70.

234

Paci

fic re

gion

0.32

90.

303

0.27

00.

361

0.20

80.

348

0.22

70.

360

Cent

ral r

egio

n0.

468

0.19

70.

580

0.24

70.

520

0.21

10.

509

0.28

8A

tlant

ic re

gion

0.12

10.

070

0.15

00.

080

0.22

60.

058

0.25

70.

117

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

Nat

iona

l Hou

seho

ld L

ivin

g St

anda

rds

Surv

ey (E

MN

V).

82

Table 4B.2 Mean and Standard Deviation, by Sector of Economic Activity and Formality Level

Primary Secondary Tertiary

Variable Formal Informal Formal Informal

Earnings 11.492 15.899 10.776 21.421 15.697Age 37.235 32.350 34.513 35.486 37.011Years of education 2.672 8.249 5.631 10.649 6.268Gender 0.916 0.609 0.710 0.517 0.456No. of children < 6 years 1.299 0.833 1.025 0.760 0.893No. of children ages 7–15 1.660 1.036 1.452 1.174 1.376Number of adults 3.318 3.419 3.516 3.470 3.358Number of elderly 0.118 0.128 0.176 0.211 0.168Nonlabor Income 3.969 10.870 6.461 14.664 9.107Managua 0.045 0.571 0.274 0.485 0.374Pacific region 0.268 0.254 0.396 0.275 0.329Central region 0.504 0.150 0.248 0.170 0.224Atlantic region 0.184 0.025 0.082 0.070 0.074

Source: Authors’calculations based on EMNV.


83

Tabl

e 4B

.3Ea

rnin

gs E

quat

ions

by

Empl

oym

ent C

ateg

ory,

200

1

Wag

e W

orke

rsEm

ploy

ers

Self-

Empl

oyed

Hou

seho

ld E

nter

prise

s

Varia

ble

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Agric

ultu

reN

onag

ricul

ture

Age

0.02

34**

*0.

0261

***

–0.0

034

–0.0

126

0.00

43–0

.001

5–0

.034

4***

0.00

65Ye

ars

of e

duca

tion

0.06

67**

0.12

79**

*0.

1374

*0.

0324

0.11

44*

0.07

31**

*0.

0692

0.02

21G

ende

r0.

053

0.22

96**

*–1

.319

–0.8

265*

*–0

.381

50.

4915

***

–0.8

999*

**0.

4435

***

Paci

fic–0

.560

1*–0

.152

5*–1

.383

–0.5

319

–1.3

6*0.

0715

0.39

33–0

.634

8**

Cent

ral

–0.4

818

–0.1

692*

–1.0

57–0

.646

4**

–0.4

303

0.10

190.

3589

–.04

77*

Atla

ntic

–0.0

964

–0.0

079

0–0

.098

90.

4632

0.48

23**

0.70

36–0

.380

8

1–0

.031

7

20.

2883

3

–1.9

96

4–1

.72*

*

50.

3646

6

–0.6

512*

*

7–0

.898

1***

8

0.20

35Co

nsta

nt0.

8791

–0.1

851

7.79

37.

476*

**1.

151

2.30

6***

4.29

3**

1.38

1

No

obse

rvat

ions

409

1,93

915

923

428

387

335

624

5R-

squa

red

0.14

710.

2465

0.13

790.

3532

0.16

850.

0539

0.09

650.

0908

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

EMN

V.

Not

e: *

signi

fican

t at 1

0%; *

* sig

nific

ant a

t 5%

; ***

sig

nific

ant a

t 1%

; lam

bdas

sta

ndar

d er

rors

are

boo

tstr

appe

d st

anda

rd e

rrors

(1–

8)

.

Table 4B.4 Earnings Equations by Sector of Employment, 2001

Primary Secondary Tertiary

Variable Formal Informal Formal Informal

Age 0.0045 0.014 0.0354*** 0.027*** 0.0253***Years of education –0.0356 0.1181*** 0.0649*** 0.177*** 0.0715***Gender 0.8978* 0.2768 0.1972 0.2212** 0.1668Pacific region 0.0546 –0.3224* –0.3854*** –0.2501** –0.0094Central region 0.8212 –0.1309 –0.405*** –0.3088* –0.063Atlantic region 1.399** –0.5493 –0.1137 –0.0783 0.2034Ï1 1.109*Ï2 1.066*Ï3 –0.0587Ï4 0.7258Ï5 0.1745Constant –1.017 –1.189 0.4249 –1.142 0.3396

No observations 1,210 219 569 693 1,812R-squared 0.0814 0.2304 0.159 0.2732 0.1097


Note: * significant at 10%; ** significant at 5%; *** significant at 1%; lambdas standard errors are bootstrapped standard errors.


85

Tabl

e 4B

.5O

axac

a-Bl

inde

r Dec

ompo

sitio

n: D

etai

led

Out

com

es fo

r Sec

tor a

nd In

form

ality

Seco

ndar

y In

form

al v

s. Te

rtia

ry In

form

al v

s. Se

cond

ary

Form

al v

s. Te

rtia

ry F

orm

al v

s. Se

cond

ary

Prim

ary

Tert

iary

Prim

ary

Seco

ndar

y In

form

alTe

rtia

ry In

form

al

Coef

ficie

nts

P-va

lue

Coef

ficie

nts

P-va

lue

Coef

ficie

nts

P-va

lue

Coef

ficie

nts

P-va

lue

Net

diff

eren

ce1.

326

0.08

31.

234

0.01

81.

598

0.17

10.

110

0.85

6

End

owm

ents

Age

–0.0

120.

245

–0.0

010.

257

0.03

00.

158

0.04

10.

000

Year

s of

edu

catio

n–0

.105

0.54

9–0

.128

0.54

9–0

.309

0.00

0–0

.775

0.00

1G

ende

r–0

.184

0.09

7–0

.413

0.09

70.

028

0.09

8–0

.013

0.01

0Pa

cific

regi

on0.

007

0.90

20.

003

0.90

2–0

.046

0.07

4–0

.014

0.04

5Ce

ntra

l reg

ion

–0.2

100.

212

–0.2

300.

212

–0.0

130.

660

–0.0

160.

050

Atla

ntic

regi

on–0

.143

0.03

8–0

.154

0.03

8–0

.031

0.16

40.

000

0.54

5To

tal

–0.6

480.

150

–0.9

220.

170

–0.3

410.

002

–0.7

780.

002

Coe

ffic

ien

tsAg

e1.

150

0.00

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190

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AG =

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, EN

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em

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AG =

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sel

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.

The objective of this chapter is twofold. The first objective is to identifythe relevant dimensions across which the labor market is segmented (inother words, which are the good jobs sectors and which are the bad jobssectors). The second objective is to analyze the role of skills in employ-ment and earnings and to see whether skills are posing a constraint toemployment growth in the good jobs sector.

The chapter is organized as follows. The first section briefly describesbasic assumptions of a model of labor market segmentation, the secondconsiders whether there is any evidence of segmentation and, if so, acrosswhich dimensions. It also points toward the possible causes of segmen-tation. The third section discusses the barriers to mobility. The last sec-tion deals with skill mismatch and the role of skills in earnings andemployment.

Labor Market Segmentation: Basic Assumptions and Literature Review

There is a growing consensus that labor markets in developing countriesare segmented, and that this segmentation has important implications forthe extent to which the poor benefit from growth and from earnings

C H A P T E R 5

Segmentation and Skill Mismatch

87

opportunities.1 At the core of this model are the following premisesregarding the country’s markets:

• The labor market consists of various segments that offer qualitative,distinct types of employment for individuals with identical productivi-ty endowments—that is, the good jobs sector and the bad jobs sector.2

• There is limited mobility between sectors and barriers of access togood jobs, so that not all those seeking work in the good jobs sectorcan find it.

• Of good job and bad job sectors, wage and employment levels are notcompetitively determined in at least one of the sectors. There are dif-ferences in marginal productivity between those two sectors.

The bad jobs sector is a free-entry sector, that is, no skills or capital andno special connections or qualities are needed to enter. Returns to laborin this sector are low, and households that earn a living in the sector aremore likely to be poorer than other households.

However, there is no consensus as to which are the segments of thelabor market and, among them, which segments should be of concernregarding pro-poor employment and labor market policies. Is the marketsegmented across the formal-informal divide? Or is it segmented acrossthe self-employment–wage-employment divide? Are labor markets seg-mented between urban and rural areas or between agriculture and nona-griculture, or both? And which of these segments are most relevant forthe poor?

Moreover, it is not easy to prove segmentation empirically because it ishard to distinguish whether workers are in a particular sector because ofchoice or lack of other employment options. For example, Maloney(2004) finds evidence that in Argentina, Brazil, and Mexico many work-ers are in the informal sector because they value the implied flexibilityand the ability to report a higher job status. These workers place a lowvalue on social security benefits because they do not believe that the gov-ernments will reliably deliver these benefits. If the analysis controls forskills, earnings in the informal sector do not differ substantially from thosein the formal sector. This has led to the conclusion that workers opt forthe informal sector because, given their level of skills, it offers them thehighest returns.

In addition to the difficulty of identifying whether markets are seg-mented and, if so, across which dimensions, it can be difficult to disen-


tangle the causes of segmentation. In many cases segmentation thatresults from noncompetitive wage-setting mechanisms may be inter-twined with extensive barriers to moving between good and bad jobs.For example, job location, skills requirements, or ethnic or gender dis-crimination may play a role.

In addition to segmentation, the size of the bad jobs sector (whether itis informal or formal) might be affected by “skill mismatch,” that is, by thefact that the available skills of the workforce do not match the skillsdemanded by the good jobs sector. For example, it has been widely notedthat technological change increases the relative demand for skills. Thisusually increases skill premiums, which may widen the income gapbetween the good jobs sector and the bad jobs sector (as has happened inother Latin American countries). In this case, the gap is not due to seg-mentation but to differences in the individual characteristics of thoseemployed in each sector, with the good jobs sector reducing its share ofunskilled workers and the redundant labor force being shed to the badjobs sector. In extreme cases, the lack of skills can become a binding con-straint to growth.

In general, the relationships between education and skills, employ-ment, and poverty under segmented labor markets are complex and arenot reduced to the role of skill mismatch. In segmented labor markets, ifjobs are rationed, it is tempting to conclude that increasing the level ofeducation of the labor force may not help those employed in the bad jobssector to get a job. Because there are no jobs, jobs are by definitionrationed. However, jobs may be rationed for the low skilled but not forthe skilled. For example, non-market-clearing wages like minimum wagesmay affect the unskilled but not the skilled labor force; and barriers tomobility due to lack of information may be stronger for the unskilled. Ifthis is the case, increasing the level of education of the labor force mayreduce the size of the bad jobs sector by pulling people out of therationed market and into the unrationed market.

Education might also be important if marginal returns to labor arehigher for the more educated. For example, there is evidence that litera-cy is correlated with increasing yields per hectare in agriculture. Better-educated laborers are better positioned to apply successfully availabletechnologies and to produce for the market rather than for self-consump-tion. Moreover, there might be externalities to education, so that anincrease in the share of workers with adequate skills increases overall pro-ductivity of the economy, as well as its competitiveness. Both may trans-

Segmentation and Skill Mismatch 89

late into higher wages for everybody, as the size of product marketsexpands. But increasing the level of education of the labor force alsoentails risks. Large inflows of skilled labor (abundant supply) may depresswages and thus dampen the earning potential of educational expansions.

Evidence of Segmentation across Different Dimensions

As mentioned above, segmentation is hard to prove empirically. The mostimmediate implication of segmented labor markets is that two apparent-ly identical individuals (with respect to education, age, experience, loca-tion, gender, and family structure) earn different wages depending on thesector in which they are working. However, this is not sufficient to estab-lish segmentation. Individuals may be in a given sector by choice. In otherwords, despite the earnings differential, being in a lower-paying sectormight give them other benefits (that is, they have different preferences).Establishing segmentation would require establishing that workers in thelow-paying sectors would rather not be there. Thus this section tries toaddress both issues.

This section explores whether markets are segmented between the for-mal and informal status of the worker (as measured by affiliation to socialsecurity), by sector of economic activity (primary, secondary, and terti-ary), and by employment category (that is, self-employed versus wageemployed, family enterprise, or employer). To do so, the analysis willexplore whether there are significant differences in returns to individualcharacteristics (such as education, gender, or age) depending on the seg-ment of the labor market.

The traditional way of establishing whether there are significant differ-ences in returns to individual characteristics is to estimate the determi-nants of earnings for each sector of economic activity as a function ofindividual characteristics. The earnings differential is then decomposedbetween the different segments, into a part due to differences in averagecharacteristics and a part due to differences in returns to individual char-acteristics using Oaxaca-Blinder decompositions. The following equationillustrates the methodology.

ln ϖi – lnϖj = βj (Xi – Xj) + (βi – βj) Xj + ((Xi – Xj) (βi – βj) (5.1)

Where ϖi denotes average earnings in sector i, βi corresponds to the vectorof coefficients from the earnings equation estimated for sector i, and Xi is


the vector of average individual characteristics (education, age, experience,and so forth). The first term on the right-hand side of equation 5.1 reflectsthe part of the earnings differential that is due to differences in (average)observed characteristics, the second term reflects that part that is due todifferences in returns to individual characteristics, and the final term is aninteraction term. Important wage differences due to differences in returnsto individual characteristics may be an indication of segmentation.

Given that people may select themselves (or be selected by employ-ers) into different segments of the labor markets according to observablecharacteristics, it is important in a first step to correct for possible selec-tion bias by estimating a sectoral choice model. That is, the probability ofbeing employed in a particular sector as a function of individual charac-teristics is determined. It gives information as to which individual charac-teristics may be potentially acting as barriers to mobility. In a second step,wages are estimated correcting for possible selection bias. From this stepit highlights the effect of education on earnings. Finally, to determine theshare of wage differentials that can be attributed to differences in therewards to individual characteristics, Oaxaca-Blinder decomposition isperformed as in equation 5.1, but with the term reflecting the earningsgap net of selection effects.3 This step will allow the identification of thepossible segments of the labor markets. The results presented correspondto the 2001 household survey.

Before a discussion of the results, it is worth describing some stylizedfacts on earnings and the composition of the labor force by segments.Figure 5.1 shows the pattern of median hourly earnings from the mainactivity for six employment categories by level of education. The classifi-cation of workers explained in the introduction has been used, but it dif-ferentiates the classifications into agricultural and nonagriculturalemployment. Agricultural employment categories are illustrated by asolid line, while nonagricultural categories appear as a dashed line. Thegraph shows a very clear divide between agricultural earnings and nona-gricultural earnings, with the latter being larger for every employmentcategory. In addition, within both sectors, being an employer offers thehighest return regardless of educational level. Within agriculture, forthose with less than secondary education, there appears to be no majordifferences between being a wage worker, a self-employed worker, or afamily enterprise worker. For higher levels of education, however, being afamily enterprise worker offers the lowest returns. For employment cate-gories outside of agriculture, among those with less than secondary edu-


cation, again family enterprise offers the lowest returns, followed by wagework and self-employment. For workers with completed secondary edu-cation, the picture changes, with wage work having the highest returns,followed by self-employment.4

These results suggest four conclusions: (i) there is a clear dividebetween agricultural and nonagricultural employment; (ii) being anemployer (that is, hiring paid labor) makes a positive difference in termsof earnings, regardless of sector or level of education; (iii) the behavior ofearnings among employment categories differs for those with less thansecondary education and for those with complete secondary or above (inthe former, there seems to be no important premium to being a wageworker, while in the latter case there is); and (iv) there appears overall tobe positive returns to education.

Figure 5.2 illustrates the evolution of median hourly earnings by sec-tor of economic activity and education. As expected, workers in agricul-ture earn less than others, except for those with a higher level of educa-tion, who earn more than workers in the informal secondary sector andin the tertiary sectors. Within primary and secondary sectors, informalworkers earn less than their formal counterparts, and the wage gap widenswith the level of education. An exception is represented by formal work-ers with no schooling in the tertiary sector, who earn almost the same asinformal workers in the secondary and tertiary sectors.

These results confirm the agricultural-nonagricultural divide, also sup-port positive returns to education, and show a premium for working inthe formal sector for workers with some level of education, with the pre-mium increasing with the level of education.

Summarizing from a simple inspection of wages across different seg-ments of the labor market, it can be concluded that there are significantwage premiums attached to being an employer and to being outside ofagriculture. Being in the formal sector carries a smaller premium thatincreases with education. The results indicate that there is scope for theinformal sector (the sector of those employed in family enterprises andthose in agriculture) to constitute the bad jobs sector.These gaps need notnecessarily imply segmentation.

For a more formal analysis of whether there is segmentation, it will beanalyzed whether differences in average returns across segments of thelabor market can be attributed to differences in returns to individual char-acteristics or differences in the average characteristics of those employedin the different segments. The first step is to estimate an earnings equation


for the different segments, correcting for possible selection bias (becausepeople might be selected or might select themselves into different seg-ments according to individual characteristics).

The first step in the estimation procedure is to understand, on the basisof observable characteristics, whether there is selection into different seg-ments. Because most people in low-income countries participate in thelabor market, the probability of being employed in a particular sectorwhile abstracting from participation decisions can be analyzed.

The probability of being employed in a particular sector should beinterpreted loosely. It might reflect both demand-side and supply-sidechoices. For example, in the case of wage workers, demand-side constraints

Segmentation and Skill Mismatch 93C

S, 2

00

5

30

25

20

15

10

5

0

waged workers agriculture waged workers nonagricultureemployers agriculture employers nonagricultureself-employed agriculture self-employed nonagriculturefamily enterprise agriculture family enterprise nonagriculture

no less than less than up to school primary secondary tertiary

education completed

Figure 5.1 Hourly Earnings by Employment Category, 2001

Source: Authors’calculations based on National Household Living Standards Survey (EMNV).

Note: “Up to tertiary” includes complete secondary, plus incomplete or complete tertiary education level.

might select the people who end up working in that sector. In the case ofmaquila factories, only workers who have completed a secondary educa-tion and are less than 30 years of age can be employed. In the case of theself-employed, employers, and family enterprises, the selection bias ismainly due to the supply side, as they are all independent workers andthey have decided to be employed in that specific sector; however,demand-side forces may still play a role if they are induced to set up andrun their own businesses because they do not find jobs as wage workers.In an analysis of the probability of being employed in a particular sector(formal versus informal; primary, secondary, or tertiary), rather than in aparticular employment category, the explanatory variables used might beinterpreted as reflecting both demand-side and supply-side constraints inthe selection process.


agriculture formal secondaryinformal secondary formal tertiaryinformal tertiary

C$

, 20

05

17.5

15.0

12.5

10.0

7.5

5.0

2.5

0 no less than less than up to school primary secondary tertiary

education completed

Figure 5.2 Hourly Earnings by Broad Sector and Informality, 2001


Note: “Up to tertiary” includes complete secondary, plus incomplete or complete tertiary education level.

To estimate how individual and household characteristics affect theprobability of getting a certain job, a multinomial logit model on 2001household survey data is estimated. In the first case, the employment cat-egories are wage workers, employers, and self-employed or family enter-prises, divided into two subcategories: agriculture and nonagriculture.There are six possible alternatives. The category of unemployed and inac-tive people is left out, since the alternative of not working is not feasiblefor most of them except for the youth and for women. In the second case,there are five categories: the primary sector, plus the secondary and terti-ary sectors divided into formal and informal subsectors. For determinatesof earnings, estimates use age, gender, educational attainment, nonlaborincome, regional dummies, and dummies for the presence of the elderly,adults, and children in the household.

The annex contains descriptive statistics of the variables used in theselection and wage equations: on average, employers, the self-employed,and household heads of family enterprises are older than wage workersand have more experience. The majority of agricultural workers aremales, as are the majority of employers (both in and outside of agricul-ture). More than one-third of those working in agriculture are illiterate,and that share increases up to almost 50 percent for the agricultural self-employed and those working in family enterprises. There is a very lowrate of illiteracy among nonagricultural workers (less than 1 percent), and40 percent have at least some primary education. Outside of agriculture,among wage workers and employers, 50 percent have a completed sec-ondary education or above, while only 30 percent of the self-employed orthose working in family enterprises have a completed secondary educa-tion or higher. Agricultural workers are concentrated in the Central andPacific regions, while others work mainly in the Managua region.

Workers in the primary sector are, on average, older and consequentlyhave more experience.5 Males are mostly employed in the primary sector,in the secondary informal sector, and in the tertiary formal sector.Illiterate workers are employed mainly in the primary sector and in theinformal subsector of both the secondary and the tertiary sector. The ter-tiary formal sector comprises mostly workers with a completed second-ary education or above, while the secondary formal sector is evenly divid-ed between workers with incomplete secondary and complete secondaryeducation or above. The good jobs, and the upper tier of the secondaryand tertiary sectors, are concentrated in the Managua region, and peopleworking there have high nonlabor incomes.


Table 5.1 illustrates the outcomes of the occupational selectionobtained by estimating a multinomial logit model.6 The reference catego-ry is wage workers in agriculture.7 The table presents the relative risk ratio(RRR), which indicates the likelihood of a worker ending up employed inthe given category (more likely if the coefficient is larger than 1, less like-ly if the coefficient is smaller than 1), compared with being a wage work-er in agriculture, if there is a one-unit increase in the explanatory variable.8

Overall, age, education, region, and nonlabor income significantlyaffect the likelihood of ending up in any given category as compared tobeing a wage worker in agriculture. Higher nonlabor income increases by9 percent the likelihood of ending up as an employer, either outside ofagriculture or in agriculture. It also increases by 9 percent the probabilityof ending up working outside of agriculture as a family enterprise or self-employed. Within agriculture it has no effect on the likelihood of beingself-employed or as a family enterprise. Thus, nonlabor income or assetsmay be acting as a barrier to moving outside of agriculture. Region deter-mines strongly whether a person ends up in an agricultural employmentcategory or not; being outside of Managua reduces the chances of endingup in agriculture. Again, this may suggest barriers to mobility from ruralto urban areas.

Education is an important determinant of being outside of agriculturefor all categories. Within agriculture, more education renders a worker 10percent less likely to work as a family enterprise worker, the lowest earn-ing category. Outside of agriculture, more education increases the likeli-hood of being an employer (by 34 percent), being a wage worker (by 34percent), and being self-employed (by 17 percent). Again, this suggeststhat education might be acting as a barrier to moving to better earningopportunities. Demographic characteristics are in general not significant,except in the case of self-employed or family enterprise workers outsideof agriculture, in which case having more children age six or youngermakes a worker less likely to be in this category.9

Table 5.2 shows the results of selection across the five categoriesdefined by sector of economic activity and informality (primary sector,secondary formal, secondary informal, tertiary formal, and tertiary infor-mal).The reference category is the primary sector.The results indicate thatbeing male decreases the likelihood of working outside of the primary sec-tor. Age increases the likelihood of working in the tertiary sector, but notin the secondary. Education is always significant and increases the likeli-hood of being employed outside of agriculture. The effect of education isslightly higher for the formal sector, with one more year of education


Tabl

e 5.

1Se

lect

ion

amon

g Em

ploy

men

t Cat

egor

ies,

200

1

Wag

e w

orke

rsEm

ploy

ers

Self-

empl

oyed

Fam

ily e

nter

prise

s

Non

agric

ultu

reAg

ricul

ture

Non

agric

ultu

reAg

ricul

ture

Non

agric

ultu

reAg

ricul

ture

Non

agric

ultu

re

Varia

bles

RRR

tRR

Rt

RRR

tRR

Rt

RRR

tRR

Rt

RRR

t

Age

1.01

82.

600

1.07

96.

880

1.09

88.

370

1.04

03.

730

1.06

37.

950

1.09

58.

290

1.10

610

.680

Year

s of

edu

catio

n1.

345

10.5

701.

066

1.58

01.

341

8.13

00.

976

–0.7

101.

171

5.57

00.

907

–2.7

901.

126

3.88

0

Gen

der

0.11

4–8

.780

1.95

21.

550

0.36

8–3

.240

0.75

3–0

.870

0.06

3–1

0.77

01.

484

1.08

00.

233

–5.2

40D

umm

y ch

ild a

ge 6

0.86

7–1

.750

0.84

6–1

.310

0.88

9–0

.870

1.07

40.

660

0.93

4–0

.770

0.98

6–0

.150

0.92

7–0

.740

Dum

my

child

7–1

51.

038

0.42

01.

038

0.36

00.

957

–0.4

400.

879

–1.3

900.

993

–0.0

801.

385

3.97

01.

115

1.14

0D

umm

y ad

ult

1.03

70.

630

1.01

90.

240

0.92

4–1

.060

0.83

6–1

.930

0.88

4–2

.070

0.99

6–0

.060

0.91

6–1

.340

Dum

my

elde

rly1.

222

1.04

01.

078

0.26

01.

221

0.69

01.

379

1.16

00.

856

–0.6

900.

827

–0.4

200.

793

–0.7

80N

onla

bor I

ncom

e1.

084

1.98

01.

096

2.24

01.

093

2.18

01.

040

0.89

01.

088

2.06

01.

081

1.92

01.

090

2.12

0Pa

cific

0.29

6–2

.430

1.24

E+08

22.9

900.

545

–1.0

801.

128

0.22

00.

315

–2.1

907.

627

1.89

00.

499

–1.3

60Ce

ntra

l0.

146

–5.2

702.

20E+

0826

.900

0.32

3–2

.590

2.02

71.

880

0.14

4–4

.660

13.8

332.

600

0.32

9–2

.790

Atla

ntic

0.20

7–4

.110

2.24

E+08

24.2

900.

404

–1.8

003.

464

2.98

00.

150

–4.3

0024

.493

3.05

00.

501

–1.7

30

Sour

ce: A

utho

rs’c

alcu

latio

ns b

ased

on

EMN

V.

Not

e: R

RR =

rela

tive

risk

ratio

s, t =

t st

atist

ics.

97

increasing the likelihood of being employed in the secondary formal sec-tor by 36 percent and in the tertiary formal sector by almost 60 percent.Nonlabor income also increases the likelihood of being outside of agricul-ture in the formal sector and tertiary informal sector.10

Overall, individual characteristics are associated with the employmentcategory and the sector of employment. Whether this reflects supply or


Table 5.2 Selection among Sectors, 2001

Secondary

Formal Informal

Variables RRR t RRR t

Age 0.989 –1.06 0.996 –0.60Years of education 1.364 8.55 1.220 8.50Gender 0.128 –7.93 0.212 –8.69Dummy child age 6 0.871 –1.68 0.903 –1.28Dummy child 7–15 0.850 –2.04 0.984 –0.25Dummy adult 0.998 –0.03 1.043 0.84Dummy elderly 0.766 –1.02 1.144 0.79Nonlabor Income 1.032 2.18 1.019 1.32Pacific 0.118 –4.70 0.316 –2.60Central 0.039 –7.64 0.113 –5.47Atlantic 0.020 –5.29 0.106 –4.85

Tertiary

Formal Informal

Variables RRR t RRR t

Age 1.021 3.00 1.016 2.96Years of education 1.588 17.13 1.264 11.02Gender 0.093 –14.56 0.073 –18.30Dummy child age 6 0.905 –1.27 0.886 –2.13Dummy child 7–15 1.026 0.36 0.973 –0.50Dummy adult 1.006 0.11 0.987 –0.33Dummy elderly 1.125 0.65 1.005 0.03Nonlabor Income 1.031 2.23 1.034 2.48Pacific 0.152 –4.55 0.200 –4.10Central 0.053 –7.32 0.076 –7.27Atlantic 0.069 –6.57 0.072 –7.22


Note: RRR = relative risk ratios, t = t statistics.

demand conditions, or both, is not possible to infer from the analysis pre-sented here. Education, nonlabor income, and region seem to play a keyrole in this selection process, increasing the chances of ending up in bet-ter-earning jobs. As such, if one is going to further explore barriers tomobility, these present themselves as good candidates for analysis.

After controlling for selection bias, the analysis of the determinants ofearnings differentials across segments of the labor markets comes into thepicture. The annex presents the results of estimated earnings equations.11

Age increases earnings for wage workers and for household enterpriseworkers in agriculture, and being outside of Managua reduces these earn-ings. Education increases returns outside of agriculture for wage workers,employers, and self-employed but has no effect on household enterpriseworkers. Within agriculture, education increases the income of wageworkers and self-employed workers. It is important to note that incomefor family enterprises is calculated as the profits from the enterprisedivided by the number of workers in the household; as such, it capturesthe returns to all factors of production, rather than to individual factorsof production. So caution is needed when interpreting the results of earn-ings equations for this group. In particular, the lack of effect of educationon household enterprise workers might just reflect this fact, rather thanno effect at all.12 The premium for education is higher outside of agricul-ture and in the formal sector of the economy.13 The above results areimportant for educational policy for two reasons: one, education may beacting as a barrier to mobility, and two, education may increase earningsin some low-earning sectors and categories.

In order to determine whether there are wage differentials across labormarket segments, the Oaxaca-Blinder decomposition is performed. (It isexplained in equation 5.1, where the term lnϖj – lnϖs corresponds toearnings gap net of selectivity effects.) The results allow identification of(i) whether the observed net wage differentials are statistically significant,and (ii) which part of the wage differentials between labor market seg-ments can be attributed to differences in average individual characteris-tics and which can be attributed to returns to observed characteristics(represented as total, endowments, and β coefficients, respectively, intable 5.3 and table 5.4).

The results indicate that there are significant wage differentialsbetween the primary and the secondary/tertiary sectors, with most of thedifference explained by differences in returns to individual characteristics(table 5.3). In all cases, differences in returns to individual characteristics



Table 5.3 Oaxaca-Blinder Decomposition by Employment Category

Coefficient P -value

Wage worker nonagriculture vs. wage worker agriculture Net difference 0.342 0.445 Endowments 0.517 0.093ß coefficients –0.336 0.480Interaction 0.160 0.617

Wage worker nonagriculture vs. enterprise agricultureNet difference 4.083 0.259Endowments –0.364 0.006ß coefficients 5.715 0.348Interaction –1.268 0.717

Wage worker nonagriculture vs. employers nonagricultureNet difference 4.409 0.001Endowments 0.248 0.000ß coefficients 4.813 0.002Interaction –0.652 0.004

Wage worker nonagriculture vs. self-employed agricultureNet difference 0.914 0.289Endowments 0.713 0.297ß coefficients 0.312 0.724Interaction –0.111 0.873

Wage worker nonagriculture vs. self-employed nonagricultureNet difference 1.122 0.001Endowments –0.255 0.000ß coefficients 1.425 0.000Interaction –0.048 0.580

Wage worker nonagriculture vs. family enterprise worker in agricultureNet difference 0.703 0.130Endowments –0.494 0.002ß coefficients 1.718 0.116Interaction –0.521 0.461Wage worker nonagriculture vs. family enterprise worker nonagricultureNet difference 0.127 0.909Endowments 0.045 0.853ß coefficients –0.102 0.928Interaction 0.184 0.497


explain most of the differential.14 In looking at which characteristics areresponsible for these differences in returns, it is age, education, and regionthat account for an important part of the difference, meaning that thereis a higher premium for education and experience outside of agriculture,with the premium lower in the Central and Atlantic regions. Wage differ-entials, however, are not significant between formal and informal sectors.Formal and informal sectors have significantly different compositions ofthe labor force, but returns to individual characteristics are not signifi-cantly different.

Table 5.4 shows the results among employment categories. The refer-ence category is wage employment outside of agriculture. Earnings differ-entials—net of selection effects—are only significant between wage work-ers and self-employed outside of agriculture and between wage workers


Table 5.4 Oaxaca-Blinder Decomposition by Employment Sector

Coefficient P-value

Secondary informal vs. primaryNet difference 1.326 0.083Endowments –0.648 0.150ß coefficients 1.205 0.110Interaction 0.769 0.090

Tertiary informal vs. primaryNet difference 1.234 0.018Endowments –0.922 0.170ß coefficients 1.065 0.058Interaction 1.092 0.108

Secondary formal vs. secondary informalNet difference 1.598 0.171Endowments –0.341 0.002ß coefficients 1.773 0.135Interaction 0.167 0.174

Tertiary formal vs. tertiary informalNet difference 0.110 0.856Endowments –0.778 0.002ß coefficients 0.398 0.512Interaction 0.490 0.049


and employers outside of agriculture. In both cases, net earnings differen-tials are positive, while observed earnings differentials are negative. Thismeans that selection is playing an important role in observed differencein average earnings among these categories. In both cases the net earningspremium is positive for wage workers owing to higher returns to individ-ual endowments, with age and education having the most importanteffect (see annex). Net earnings differentials are not significant betweenother employment categories and wage workers outside of agriculture,which means that differences in observed earnings arise solely because ofselection effects.

Thus, there appears to be segmentation between the primary and sec-ondary/tertiary sectors, or in other words, between employment in agri-culture and nonagriculture. Potential segmentation between the formaland the informal is not supported by the results. Potential segmentationbetween wage workers and self-employed workers and between wageworkers and employers outside of agriculture is also supported by theresults. Differences in earnings between other employment categoriesarise mostly as a result of selection, whereby more educated and olderworkers select themselves or are selected into higher-earning employ-ment options.

Evidence from section A suggests that selection into employment out-side of agriculture plays an important role in explaining observed earn-ings, with education playing the most prominent role in this selectionprocess. Even after a netting out of the effects of selection from the nona-gricultural earnings premium, there is an important premium to workingoutside of agriculture, with most of the difference being explained by dif-ferences in returns to endowments, mainly age, education, and location.Therefore, some potential segmentation between agricultural and non-agricultural employment is supported by the data. Potential segmentationbetween formal and informal work is not supported by the data. Instead,selection effects appear to explain the observed earnings differentials.Selection into formal employment is determined mostly by educationand gender, with more education and being a male increasing the chancesof selecting or being selected into formal employment.

In terms of employment categories, the results suggest that most of theobserved earnings differentials can be attributed to selection effects. Oncethese effects are controlled for, there are no significant differences amongmost of the employment categories, except for earnings premium in wagework and self-employment outside of agriculture. Selection into better-


earning employment categories is determined by education, age, and non-labor income. Nonlabor income appears to be an important determinantfor being an employer, which may be an indication of credit constraints,as those with other sources of income may also be able to offer more ascollateral.

Thus, in terms of policy it is important to understand the barriers toselection into formal employment and wage work and to moving peopleoutside of agriculture. In addition, credit constraints to becoming anemployer merit further research.

As has been mentioned, however, differences in returns to individualcharacteristics among segments of the labor market are only a first step inidentifying segmentation. These differences may be capturing other non-pecuniary characteristics of jobs (such as flexibility or regional price dif-ferences), which are not explored here. In addition, the differences areinconclusive about whether workers end up in a given segment becauseof choice or through lack of an alternative. These issues are explored fur-ther in section B.

Segmentation and Barriers to Mobility: A Qualitative Approach

The previous section showed that when controlling for observable char-acteristics, markets may be segmented between agriculture and nonagri-culture, among formal and informal secondary sectors, and amongemployers and nonemployers.

However, this is not sufficient for an understanding of whether there issegmentation among good and bad job sectors. For segmentation to bepresent, it is necessary that workers not be in the low-paying alternativesthrough choice. Unfortunately, this is a question that is rarely addressed inhousehold surveys. However, the Nicaragua household survey contains aquestion that addresses this issue for the nonagricultural self-employed. Inparticular, the 2001 survey includes a question that may help to identifywhether the nonagricultural self-employed are self-employed becausethey cannot find a wage job. Those who respond as being self-employed inthe nonagricultural sector are asked detailed questions about the type ofbusiness they have. Among these questions they are asked the reason foropening a business. One possible answer is “because [they] could not findwage employment.” In other words, they would rather be wage workers.

Table 5.5 summarizes evidence from the survey question reportedabove, disaggregating the results by level of education, and table 5.6 sum-


marizes the same evidence by poverty level. For the population as awhole, the main reason for starting a business is because it gives workersmore flexibility, and this is followed by wanting to complement othersources of income.

However, when the answer is disaggregated by level of education, it isfound that for those with no schooling or with incomplete primary edu-cation, the most important reason for starting a business is to comple-ment other sources of income, followed by the inability to find wageemployment. It should also be noted that a very low proportion of low-skilled workers answer that they started a business because they couldhave a higher income than as a wage worker. Thus, self-employment doesseem to be a last-resort option for 23 percent of those with low skills.

When the results are disaggregated by poverty level a similar pictureemerges. Twenty-six percent of the poor started a business because theywere unable to find wage employment. However, 28 percent did sobecause of the flexibility it allowed them. Only 3 percent started a busi-ness because they believed that they could have higher earnings than aswage workers.

This suggests that some sort of segmentation may be occurring for thosewith very low skills, which is consistent with the evidence in section A.


Table 5.5 Reason for Starting a Business, by Level of Education, 2001(percentage who state as main reason)

No Incomplete Complete Incomplete Complete Schooling Primary Primary Secondary Secondary Tertiary All

Implied flexibilitya 23 32 39 36 35 35 33Get higher income 4 5 6 11 10 12 7

than as wage employment

Complement other income 35 37 25 26 32 21 31

Could not find wage 23 17 20 17 18 12 18employment

Wanted to use skills 2 1 1 4 1 10 2Other 13 9 9 5 5 11 9Total 100 100 100 100 100 100 100


a. “Implied flexibility”aggregates the following answers: “wanted to be independent,”“flexible time,”and “combinework and households activities.”

The result may be intuitive: if minimum wages are binding, those withproductivity below the minimum wage would be the ones rationed out ofwage employment (most likely workers with very low skills).15

The agricultural business module does not have a comparable question(regarding their reason for opening the business), so it is unclear to whatextent agricultural self-employment may be serving as a last-resortemployment option. But the Nicaragua poverty assessment conducted aqualitative study from which some insight may be gained as to possiblesegmentation and barriers to mobility (Del Carpio 2006). The studyfocused on urban and rural communities that, on average, hovered aroundthe poverty line, but both poor and better-off households were inter-viewed. The study found some evidence that there are barriers and queu-ing in accessing formal jobs and that agricultural employment is not a pre-ferred employment option, particularly among the youth.

Access to formal employment seems to be limited by geography, skills andsocial connections. From the communities evaluated in the qualitative analy-sis there seems to be a pattern emerging in terms of formal employment;people in the urban area tend to have wider access to formal low skilled jobslocated in nearby communities or urban centers. People in an urban commu-nity in a municipality in Managua were able to count and report the statuson 10 people in the community who are formally employed in a localcement factory. Those people have social security benefits and have a fixedincome whereas the rest of the people in the community work informally asdrivers, carpenters, builders, welders (all men) and making tortillas, bread,washing, cleaning and domestic duties (for women) People who gained


Table 5.6 Reason for Starting a Business, by Poverty Level, 2001(percentage who state each reason)

Poor Not Poor Total

Implied flexibility 28 34 33Get higher income than as wage employment 3 9 7Complement other income 33 30 31Could not find wage employment 26 15 18Wanted to use skills 1 3 2Other 9 9 9Total 100 100 100


employment in the cement factory or maquilas usually have someonealready working who helped them gain employment; the majority of thepeople however cannot access these jobs because they either lack the skillsnecessary, a connection or both. In RAAS [the Autonomous Region of theAtlantic South], the ability to speak English is a necessary skill to work as anembarcado (in cruise ship); people with low levels of education, ability tospeak English and an initial fee for paperwork (passport, medical exams,etc.) can access these jobs. In Managua, a high school degree is required towork in the maquila factories; age (under 30) is also a factor that some ofthe youth mention is a requirement. People in some rural communitiesexhibited frustration toward the lack of employment outside of agriculture;many are hopeful about the prospect of finding a job, particularly the youthwho often aspire to work in an activity different than that of their father(agriculture).There is wide heterogeneity in economic functions and oppor-tunities related to agriculture; disparities within communities and betweenthem as well as gender divisions. Livestock and commerce of livestock prod-ucts (milk, cuajada, meat) are generally reserved for the more affluent mem-bers of the community.31 (Del Carpio 2006, 5).

In addition, the sector of employment was perceived by the inter-viewed communities, together with the available services and education-al attainment, as important factors in determining the socioeconomicchange. The youth in particular expressed their concern for the lack ofadequate employment opportunities and for the little access they have tovocational training; they see agricultural work as the only available occu-pation. They want to have a better life than their parents and would liketo have a profession but see no alternatives, no one has come to help themor worry about them” (Del Carpio 2006, 22).

Throughout the qualitative analysis, a rural-urban divide was present.Rural communities have more limited access to nonfarm employmentand to education. Both factors reinforce each other and act as barriers forthe poor in moving out of poverty through better employment opportu-nities. Although communities perceive education to be important foraccessing better earning opportunities, the qualitative evidence is that thesupply of educational services is vastly reduced in rural areas, and partic-ularly in remote ones. The transport costs for accessing secondary educa-tion are high, and in the winter months the lack of infrastructure makesit impossible for children and teachers to reach schools. In one of the ruralcommunities interviewed, parents do not send their daughters to school


because they perceive their daughters to be at risk, given the long dis-tances that they must travel.

All of the above factors suggest that rural farm employment may be anonpreferred sector of employment, and that skill and geographic barri-ers reduce outward mobility for this sector. Formal wage employment, onthe other hand, seems to be an option mostly for urban workers.Although many opt for self-employment as a choice rather than as a last-resort option, an important fraction, particularly those with low skills,prefer self-employment to wage employment. Queuing is evidenced bythe need for connections in accessing formal wage employment. Finally,lack of skills seems to be a major obstacle to good employment opportu-nities. The next section explores the skill mismatch in more detail.

Skill Mismatch

As mentioned at the beginning of this chapter, in noncompetitive settings,supply and demand may not equate to “clear the market.” Instead, mar-ket imperfections and friction may imply that a share of those needingemployment end up unemployed or, if unemployment is not an option,in the bad jobs sector. This means that markets would be segmented inthe sense defined in section B; that is, two agents with similar character-istics would end up with different earnings depending on their segmentof the labor market (in the good jobs sector versus unemployed or in thebad jobs sector).

However, although segmentation implies that there may be two iden-tical individuals with different earnings because they are in different seg-ments, it is also well known that the compositions of labor market seg-ments differ, with the good jobs sector having a lower share of unskilledworkers than the unemployed sector and the bad jobs sector. A possibleexplanation is that the rigidities that generate the segmentation are morebinding for the unskilled. For example, minimum wages or union-setwages affect the unskilled more than the skilled. If workers in the bad jobssector could become skilled, they would have a higher probability ofbeing hired in the good jobs sector. In terms of demand and supply, thismeans that excess supply is higher for the unskilled population. This iswhat is referred to as mismatch of skills.

Skill mismatch matters not only in a static sense but also and probablymost importantly, in a dynamic sense. In the presence of skill-biased tech-nical change, skill mismatch is likely to increase. The larger the share of


agents that lack or cannot acquire the skills demanded by firms, the largeris the share that will end up in the bad jobs sector. Rising informality orincreasing shares of employment in agriculture may be consequences ofrising skill mismatch.

There are several ways of exploring the degree and evolution of skillmismatch.This section reviews two of these methods.The first is an adap-tation based on the work of Katz and Murphy (1991). The general idea isthat if the relative demand of two educational categories is stable, then anincrease in the relative supply of a group must lead to a reduction in therelative wage of that group. For example, if the amount of skilled labor isincreasing with respect to unskilled labor, and if there is no change in thedemand for either, then the wages of the skill must fall in response to thegreater relative supply. Thus, a very simple test of this hypothesis is tocheck whether the product of the change in the skill premium and thechange in the relative supply is negative. Rising wage gaps due to relativedemand shifts may be interpreted as increasing skill mismatch (that is, thedemand for skills is rising more rapidly than the supply of skills).

Aside from supply and demand, average wages may be affected by theaverage level of education. If the quality of education is changing uneven-ly (for example, if it is improving for the unskilled), then the wage pre-mium may go down for the youngest cohorts, and this will be reflected inan overall decrease in the skill premium. However, this shrinking of theskill premium would not be caused by the relative supply but by thechanging quality of education.To control for this effect, it is useful to per-form the analysis by cohorts, as the quality of education is likely to stayconstant within cohorts.

Figure 5.3 and figure 5.4 show the changes in the relative supply ofskills and the skill premium by cohort of workers for the total populationand for the population in urban areas. The skilled are defined as thosehaving a complete secondary education and above. Cohort 1 correspondsto the population ages 15 to 25 in 2001; cohort 2 corresponds to the pop-ulation ages 26 to 35; cohort 3 corresponds to the population ages 36 to45; and cohort 4 corresponds to those between 46 and 55. The fifth andlast cohort, those between 56 and 65, is not included in the analysis, giventhat an important fraction was above working age (65 years) in 2005.

The results of the skill mismatch investigation show that, for the totallabor force (urban and rural), the skill premium between 2001 and 2005shrank for the youngest cohort while it remained relatively constant forthe other cohorts. For cohorts 2 and 3, the skill premium increased, and


Segmentation and Skill Mismatch 109p

erce

nt

chan

ge

120

100

80

60

40

20

0

-20

-40 cohort 1 cohort 2 cohort 3 cohort 4 ages 15–25 ages 26–35 ages 36–45 46–55

skill premium

relative supply

Figure 5.3 Changes in the Skill Premium and the Relative Supply of Skills, of Total Wage Workers, 2001–05


for the oldest workers it decreased. When the results are analyzed for theurban labor force only, the results are similar.

General features are worth highlighting. First, the educational expan-sion is significant. This is manifested in the fact that, in the case of urbanareas (for example, for 2005), 79 percent of the urban population ages 19to 29 (cohort 1) had complete secondary education or above, whereasonly 27 percent among those ages 50 to 59 (cohort 4) had a secondaryeducation or above. A similar trend is observed for the total population.The second feature of these results is that, proportionally, in most caseswhere there was a drop in the skill premium, the reduction of the skillpremium was substantially smaller than the increase in the relative sup-ply of skills. In other words, the elasticity of the wage with respect to thesupply of skills was substantially smaller than 1, or relative wagesresponded less than proportionally to relative labor supply, or the demand

for skills was increasing for the period analyzed. Third, it is worth high-lighting that the skill premium is lower in the younger cohorts, where therelative supply of skills is highest, which reinforces the idea that the high-er supply of skills is reducing its relative return.

The above analysis suggests that the demand for skills in the wage sec-tor is increasing. In the case of cohorts 2 and 3, the evidence suggests thatthe supply of skills, although increasing slightly, is increasing at a slowerpace than the relative demand. For the oldest cohort (4) the result is theopposite. In the younger cohorts, however, the increase in the supply oflabor was large enough to have had a downward impact on the relativewage. This may imply that, as the educational expansion continues, thewage differential could possibly be further reduced. The implications forpoverty reduction will depend on whether this contraction of the skill

110 Making Work Pay in Nicaragua: Employment, Growth, and Poverty Reductionp

erce

nt

chan

ge

140

120

100

80

60

40

20

0

-20

-40 cohort 1 cohort 2 cohort 3 cohort 4 ages 15–25 ages 26–35 ages 36–45 46–55

skill premium

relative supply

Figure 5.4 Changes in the Skill Premium and the Relative Supply of Skills, of Urban Wage Workers, 2001–05


premium is obtained through higher wages for the unskilled or lowerwages for the skilled, and on whether the educational expansion is reach-ing the poor. If the educational expansion is leaving the poor behind, andthe skill premium is shrinking because of lower wages among the skilled,then poverty is not likely to be reduced. If, however, part of this educa-tional expansion is reaching the poor, and the skill premium shrinksbecause of higher unskilled wages, then poverty should be reduced. Forthe period analyzed, wages for the unskilled increased in constant terms,thus potentially having a poverty-reducing effect.

Despite some small increases in the skill premium of those ages 36 to46, it is unclear whether the economy has a substantial skill mismatch, asthe observed increases in the skill premium are small, and among theyounger cohorts the skill premium has shrunk. On the other hand, thesupply of skills seems to be growing, particularly among the youngestcohorts.

In addition to exploring the evolution of the skill premium and thesupply of skills, a more direct approach to discovering whether the sup-ply of skills meets the demand is by asking employers whether they findit difficult to fill vacancies because of lack of skills. Although there is nosuch information for Nicaragua, the 2003 Investment ClimateAssessment asks a related question, namely, whether the skills and theeducation of available workers present a problem for the operation andgrowth of business. The survey asks the respondents to judge the severityof the problem on a five-point scale, with ratings of no obstacle, minorobstacle, moderate obstacle, major obstacle, and very severe obstacle.

Table 5.7 presents the results of the question, to what extent skills andeducation of available workers are a problem for the operation andgrowth of business, disaggregating firms by firm size, export type, andownership. For the aggregate, only 5.5 percent of firms judged it to be avery severe problem, and 11.5 percent judged it to be a major problem.The majority of firms (66 percent) considered it not a problem or only aminor problem. When the results are disaggregated by firm type, theyindicate that large firms, exporters, and government firms tend to find itslightly more of a problem, although the majority consider it a minor ora moderate problem. The results seem to confirm what the analysis ofskill premiums and supplies indicated.



Table 5.7 Skills and Education of Available Workers as an Obstacle to Firms’ Opera-tion and Growth, 2003

Firm size by number of workers

Response Small (1–19) Medium (20–99) Large (100+) Total

Not a problem 57.5 38.7 40.0 50.2Minor problem 15.6 18.2 17.5 16.6Moderate problem 13.8 19.0 22.5 16.1Major problem 9.1 14.6 17.5 11.5Very severe problem 4.0 9.5 2.5 5.5

By export type

Exporters Nonexporters Total

Not a problem 38.0 52.5 50.2Minor problem 23.9 15.2 16.6Moderate problem 21.1 15.2 16.2Major problem 12.7 11.3 11.5Very severe problem 4.2 5.8 5.5

By ownership

Domestic private Foreign Government 50–50 Total

Not a problem 51.1 47.2 42.9 28.6 50.2Minor problem 17.0 13.9 14.3 14.3 16.6Moderate problem 15.7 16.7 14.3 28.6 16.2Major problem 11.0 16.7 14.3 14.3 11.5Very severe problem 5.2 5.6 14.3 14.3 5.5Total 100 100 100 100 100

Source: Authors’calculations based on World Bank Enterprise Survey.

Note: 50–50 is foreign domestic ownership.

Notes

1. Labor market segmentation is now part of the standard labor economic text-books; see, for example, Borjas (1996), Bosworth, Dawkins, and Stromback(1996), and Layard, Nickell, and Jackson (1991). The main reason is that itoffers a better explanation for some empirical observations than the compet-itive model. An often-quoted example is the persistent existence of intrain-dustry wage differentials for observationally equivalent workers (Katz andSummers 1988). For other contributions, see Dickens and Lang (1985) andEsfahani and Salehi-Isfahani (1989).

2. The bad jobs sector is usually associated with the agricultural sector or theinformal sector, and the good jobs sector is generally associated with theindustrial or modern sector or the formal sector.This distinction may oversim-plify; the division of the labor market between good and bad jobs goes beyondthe formal versus informal or agricultural versus industrial divides anddepends on the specificities of each country.

3. See Newman and Oaxaca (2004) for a discussion of earning gap decomposi-tion in the presence of selection.

4. The graph does not show earnings for family enterprises and self-employed inagriculture for workers with complete secondary or more because there werevery few observations in each cell.

5. Experience is constructed as age minus years of education minus six.

6. It is a generalization of the two-step selection-bias correction method intro-duced by Heckman (1979) that allows for any parameterized error distribu-tion. It only requires the estimation of one parameter in the correction termthat is achieved at the cost of some restrictive assumptions, namely, linearityin the outcome variable and joint normality in the error terms (seeBourguignon, Fournier, and Gurgand 2007).

7. The analysis has left out the no-education dummy and the Managua dummyto avoid perfect collinearity.

8. Relative risk ratios (RRRs) are presented as an exponential; thus a coefficientlower than 1 indicates a negative effect and a coefficient larger than 1 indi-cates a positive effect. For example, the first column in the table indicates thatan increase of one year in age makes a worker 1.02 times more likely to endup employed as a wage worker outside of agriculture than to end upemployed as a wage worker in agriculture.

9. Some of the results suggest that a sequential decision might be taking place.However, this possibility was examined by testing IIA assumptions. In all butone case IIA assumptions held, so further nested models were explored.


10. Caution should be exercised in inferring causality; it might be that because aworker is outside of agriculture the worker’s nonlabor income is higher, ratherthan the other way around.

11. Earnings functions are separately estimated for the two classificationsdescribed above. The dependent variable is the logarithm of hourly earnings;the impact of estimated coefficients is measured in terms of percentagechange in hourly earnings for a unit increase in the explanatory variable. Sincean analysis controls for selection bias by using a number of variables that canbe interpreted as demand- and supply-side constraints, only standard controlsother than the selection term λ are included in the wage equations.

12. There are other approaches to calculating returns to labor for householdenterprise workers. One approach is to estimate a profit function, with differ-ent types of labor forces—female adult, female young, male adult, maleyoung—in efficiency units (that is, adjusted by years of education of each),and then impute returns to labor to each type of worker. Alternatively, insteadof using as regressors the head of the enterprise’s individual characteristics,one might use the average characteristics of the household.

13. Again the lack of returns to education in agriculture overall may reflect thefact that household enterprise workers are almost 50 percent of all workers inthis sector.

14. For a more detailed analysis, the tables in the annex report Oaxaca-Blinderdecompositions for each variable.

15. The analysis assumes that more-skilled workers are more productive, and thatin the medium and long run, diminishing marginal returns to labor due tofixed capital need not set in. If in fact capital is fixed, then the marginal prod-uct of labor will also depend on the employment decisions of the firm (thatis, how many workers to hire).


Despite modest economic growth and important employment growth,Nicaragua saw no major decrease in poverty. Two main factors explainthis outcome. One, an important fraction (39 percent) of new jobs wasgenerated in agriculture, which offers the lowest returns among econom-ic activities. Two, jobs with good earnings, generated outside of agricul-ture, either were accessible only to the most educated workers, as in thecase of the maquila sector, or experienced a decrease in wages, as was thecase for the industrial food and beverage sector.

The depth of poverty, as measured by the poverty gap, saw significantreductions. These reductions in poverty were due to the following:increases in the relative prices of products produced by the agriculturalpoor, and an increase in the amount of remittances. However, the increasein income was not sufficient to bring the poorest out of poverty.

The analysis presented here has implications for action on five policyfronts: (i) skills, (ii) productivity, (iii) employment generation, (iv) geo-graphic mobility, and (v) regulation of minimum wage.

Skill levels in Nicaragua are substantially lower than in neighboringcountries. Despite the important progress on this front, substantial efforts

C H A P T E R 6

Policy Implications and Further Research

115

are still needed. Increasing skills in Nicaragua would involve both bene-fits and risks, and any education policy must try to magnify the formerwhile minimizing the latter. Currently, the supply of skills seems to begrowing at a higher rate than the demand, and, as a consequence, wagesfor the skilled population might drop. Such a drop in the returns toschooling could serve as a disincentive to acquiring education and mightreduce employment growth in the waged sectors of the economy, main-ly manufacturing and services. It is thus imperative that policies toincrease growth in these sectors (and with this, to increase the demandfor labor) be undertaken.

Currently, constraints to growth appear to lie outside of the labor mar-ket and involve macroeconomic uncertainty and lack of affordable cred-it. An increase in the demand for wage work and, in particular, for higherskills is unlikely to be seen unless these constraints are addressed.However, higher skills are important determinants of earnings among theurban self-employed and family enterprises, as well as among urban infor-mal wage workers. Higher skills are also important in increasing earningsin wage agricultural work and among agricultural employers. Targetingthe expansion of education to the rural sector appears to have importantpotential as a poverty-reducing strategy. Education is a key determinantin accessing better earning opportunities and in moving out of agriculture.

Productivity in Nicaragua declined during the period studied, withimportant decreases within agriculture. Nicaragua has the lowest levels ofagricultural productivity among its neighbors and trading partners. Thisfactor, together with some indirect evidence of low mobility betweenurban and rural areas, suggests that raising productivity in agricultureshould also be in the forefront of policy initiatives. Without targetedinvestments in agricultural productivity and agricultural exports, decreas-ing rural poverty in the short and medium runs seems implausible.

Employment generation should be targeted toward the formal second-ary and tertiary unskilled labor–intensive sectors. Exploring targetedinterventions to foster growth in such sectors as tourism, with trainingprograms for the unskilled specifically designed for the industry, seems apolicy worth exploring. Nicaragua recently conducted a tourism invest-ment climate survey that may provide initial input for the design of thispolicy. Regulation does not seem to pose a constraint for job creation andgrowth. The most urgent policies in this area would seem to be to addressmacroeconomic uncertainty and credit constraints.


The study pointed toward possible geographical barriers to mobilitybetween the urban and the rural sectors. It is as yet unclear how impor-tant these barriers to mobility are, and what the main determinants of thebarriers are. Further study on this issue may yield promising policy impli-cations. Becoming an employer is also linked with availability of nonlaborincome, which leads to the natural question of whether alleviating creditconstraints might help more family enterprise workers or self–employedto become employers.

It is unclear to what extent the current minimum wage structure inNicaragua provides any benefits or manages to take into account the skillsof the labor force. More productive sectors have higher minimum wagesand thus may be constraining the poorest from accessing jobs in precise-ly the sectors that offer the highest earnings potential. Workers with pro-ductivity below the minimum wage will be rationed out of formalemployment. The higher the minimum wage is, the less access theunskilled have to these sectors. Results of this study suggest that mini-mum wages may be binding for the maquila manufacturing sector andprobably for commerce. Further study in this area may clarify the extentto which the current sectoral structure of minimum wages is beneficial tothe poor or is actually constraining them from accessing jobs in the mostproductive sectors of the economy.

Policy Implementations and Further Research 117

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Besley, Timothy, and Robin Burgess. 2004. “Can Labor Regulation HinderEconomic Performance? Evidence from India.” Quarterly Journal of Economics119 (1): 91–132.

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Bourguignon, François. 2002. “The Growth Elasticity of Poverty Reduction:Explaining Heterogeneity across Countries and Time Periods.” DELTAWorking Papers No. 2002–03. Département et Laboratoire d'EconomieThéorique et Appliquée of the École Normale Supérieure, Paris, France.

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Aagriculture sector, 59

area harvested, 74earnings, 23, 91, 92, 99education and, 91, 92, 95, 99, 114n.13employment, 12, 13, 69–76, 107growth, 41, 73income, 65, 68new jobs, 115output, 73poor vs nonpoor, 65productivity, 11–12, 40, 69, 74self-employment, 74, 77n.2, 105, 107value added, 39–40workers, 18, 95, 96, 97, 100

B

bad jobs sector, 88, 89, 113n.2beans, dry, 69

productivity, 70, 72benefits, 60, 63business, reason to start, 114n.15

education and, 104–105

C

capital-labor ratio, 58cereals, prices, 76child labor, 4, 12coffee, 69

prices, 74productivity, 70, 72

commerce sector, 31, 117community services sector, minimum wage,

23–24costs, nonwage labor, 20country context, 7–26

D

definitions, 4–5demographics

characteristics, 96, 113n.9shifts, 56transition, 8–9

dependency rates, 66, 69, 26n.1

Index

125

E

earnings, 5, 92analysis, 114nn.10, 11determinants, 99economy, 7education and, 92employers, 5employment category, 13–15, 83employment selection and, 102functions, analysis, 114n.11hourly, 91, 93household enterprise, 5informality, 94labor market segmentation and, 90sector, 84, 94self-employed, 5, 13

education, 12, 29–30, 36–37, 116access to, 106–107agricultural earnings, 91, 95, 114n.13earnings and, 99employment sector and, 18, 96employment categories, 91level of, and reason to start a business,

104–105maquila sector, 94obstacle to firms’ operation and growth,

112population ages, 30self-employment, 92skills and, 89–90

mismatch, 108, 109premium, 111

emerging markets, productivity analysis,26n.5

employed, 4employment, 1

generation, 49–50, 115, 116–117intensive growth 2

rateagriculture, 66changes, 56

shifts in, 58structure, 10–11subsector, 43trends, 30–31

employment categoriesearnings

differentials, 102–103equations, 83hourly, 93

education, 91labor market segmentation, 90mean and standard deviation, 81Oaxaca-Blinder decomposition, 86, 100poverty level, 62quintile, 62selection among, 96, 97

employment sector, 32, 34earnings equations, 84employment generation, 49–50Oaxaca-Blinder decomposition, 101productivity and, 40

enterprise survey, 20error distribution, 113n.6experience, 113n.5export processing zone (EPZ) , 45–46exports, 9, 73–74

products, 69producer prices vs consumer prices, 75

F

family enterprise workers, 100manufacturing, 43, 45

financial intermediation, 10formal sector, 4, 98

access to, 105, 106, 107benefits, 60, 63, 105education, 106labor market segmentation, 90mean and standard deviation, 82Oaxaca-Blinder decomposition, 85, 101wages, 22–23

G

GDP, 8, 10growth, 9, 27

aggregate, decomposing, 48––49manufacturing, 45sectoral, 28, 41

growth rates, past, 8

H

HIPC Initiative, 8household enterprise worker, 4

earnings, 14, 15returns to labor, 114n.12

household incomechange per capita, by quintile, 66, 68, 69growth, 59


household survey, 47n.2hurricane, emergency assistance and

reconstruction, 8, 28

I

inactive, 4inactivity, 60income

agriculture, 65, 68average, 1employment profile and, 60–63growth, 1, 6n.1

decomposition of, 32–42levels, 2self-employed, 3, 6structure

by poverty level, 64by quintile, 64

individual characteristics, 98–99industrialization, 11, 12, 18–19informal sector, 12, 60, 98

labor market segmentation, 90mean and standard deviation, 82Oaxaca-Blinder decomposition, 85, 101wages, 23

International Monetary Fund policies, 8investments, 8, 9, 75

agriculture, 12climate, issues, 22constraints to, 20–21investments

J

jobs, two or more, 13

L

labor force, 4growth, 30profile, by quintile, 66, 67regulations, 20–25

labor income, 5decomposition, 63–69, 77n.2profile, 59

labor income growthdecomposition, 65, 78–80per capita, 63, 65, 70Shapley decomposition, by quintile, 70

labor market, 2, 4, 107context, 10–19indicators, 13premises, 88

labor market segmentation, 87–107,113n.1

analysis, 92–95barriers to mobility and, 103–107causes, 88–89distinguishing, 88evidence of, 90–103primary vs secondary sectors, 102wage workers vs employers, 102wage workers vs self-employed

workers, 102life expectancy, 7low earner, 5

M

macroeconomic context, 7–10indicators, 11

Macroeconomic Policies for Growth andEmployment, 6n.3

manufacturing sector, 31, 42–47, 117employment generation, 44growth, 41, 45productivity, 40subsector, 43value added, 39–40wages, 44

maquila sector, 4, 9, 43–44, 117education, 94employment and output, 46evolutuion and importance, 45–46growth, 10

marginal cost, 25meat, prices, 75migration, 12milk, 69

productivity, 70, 72minimum wage, 21–25, 117

analysis, 22–23mobility, barriers to, 103–107, 117modern sector, 11–12multinomial logit model, 96, 97, 113n.6

N

new jobs, 115

Index 127

O

Oaxaca-Blinder decomposition, 90–91,99–103

employment categories, 86sector, 85

occupational selection, 96, 97output per worker, 56

changes, 57aggregate and by sector, 52–54within, 57

decompositionper sector, 50–52per worker, 36–37

trends, 27–28

P

participation rates, 66, 69, 77n.3policy, 2

implications, 115–117selection barriers and, 103

poor employed, 27population, 7

ages 60–64, 47n.1growth, 9–10hierarchical description, 16–17inflow, 28labor profile, 59rural, 18–19structure, 14, 15, 29trends, 28–30urban vs rural, 19

povertygap, 115impact of growth, 6n.2labor profile, 65rate

unemployment and, 12working-age population, 32, 33

trends, 31–32Poverty Reduction and Growth Facility

(PRGF), 7prices

agricultural products, 73cereals, 76exports, producer vs consumer, 75income and, 75meat, 75sensitive products, 76

price shocks, 75

primary sector, 18, 95, 96labor market segmentation, 102Oaxaca-Blinder decomposition, 101

productivity, 116agriculture, 69, 70–71, 72employment and, 38–39

sectors, 40shares, 40

poor, 69self-employed, 69total factor (TFP) , 36–37, 58U.S. , 75

public transfers, 63

R

regulations, 2relative risk ratios (RRRs), 96, 113n.8remittances, 63report

objective, 2–3structure, 3, 5

research, further, 115–117returns to labor, 1rice, 69

productivity, 70, 72rigidities, 107

indexes, 20, 21rural-urban divide, 106

S

secondary sector, 10, 18, 95, 96, 98labor market segmentation, 102Oaxaca-Blinder decomposition, 85, 101

sector of economic activitylabor market segmentation, 90mean and standard deviation, 82

sectorsdecomposition, 38–41employment increases, 56growth, 39intersectoral shifts, 54–55, 57

decomposition of, 38, 39movement across, 38selection among, 98

self-employed, 4, 10, 11, 26n.4, 60,63, 100

agricultural, 68, 74, 77n.2earnings, 13, 14, 15education and, 92


income, 66manufacturing, 43number, 18

sensitive goods, 69–70area harvested, 73farms by number and size, 71prices, 76productivity, 71, 74

services sector, growth, 41Shapley decomposition, per capita labor

income, by quintile, 70skills

levels, 115–116mismatch, 89, 107–112

analysis, 108–111obstacle to firms’ operation and growth,

112premium, 109–111supply of skills and, 109, 110–111urban workers, 110

social security, 12, 20supply and demand, skill mismatch, 108,

109–110survey, household, 47n.2

T

tertiary sector, 10, 18, 95, 96, 98Oaxaca-Blinder decomposition, 85, 101

TFP, 36–37, 58trade and integration agreements, 8transformation services, 26n.2

U

unemployed, 4, 26n.3, 60rates, 10, 12rural vs urban, 32urbanization and, 11

urban workers, skill premium vs skills supply, 110

urbanization, unemployment and, 11

V

value added, 27growth, 38–40, 41

decomposition of, 48–58per capita, 33, 48–58

decomposition of, 34–35

W

wage employment, 12, 60access to, 107skill demand, 110–111

wages, 5, 11behavior of, 42differentials, analysis, 99–103disparity, 90hourly, 22, 24manufacturing sector, 44minimum, 21–25, 117sector, 42skill mismatch, 108structure, 117

wage workers, 5, 60, 100benefits, 20earnings, 14, 15manufacturing, 43, 45

working-age population, 5, 9–10, 12, 31,77n.2

employment status, 60, 61poverty rates of, 32, 33

Index 129

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D I R E C T I O N S I N D E V E L O P M E N T

Poverty

Making Work Pay in NicaraguaEmployment, Growth, and Poverty Reduction

Catalina Gutierrez, Pierella Paci, and Marco Ranzani

Poor people derive most of their income from work. However, there is insufficient under-standing of the role of employment and earnings as a link between growth and povertyreduction, especially in low-income countries. The Making Work Pay series analyzes theimportant roles of labor markets, employment, productivity, and labor income in facilitatingshared growth and promoting poverty reduction.

Making Work Pay in Nicaragua provides a description of the trends in growth, poverty andlabor market outcomes in Nicaragua. It assesses the linkages among changes in output,employment, and labor productivity and links changes in the quality and quantity ofemployment to poverty reduction. The book also addresses other key issues such as ruralversus urban conditions, women and children in the labor market, self-employment andhousehold enterprises, and it identifies priorities for further analysis and policy intervention.

Making Work Pay in Nicaragua will be of interest to development practitioners in interna-tional organizations, governments, research institutions, and universities with an interestin inclusive growth and the creation of productive employment.

ISBN 978-0-8213-7534-1

SKU 17534

Making Work Pay in Nicaragua: Employment, Growth, and Poverty Reduction (Directions in Development)

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Transcript of Making Work Pay in Nicaragua: Employment, Growth, and Poverty Reduction (Directions in Development)